DynamicPPL

AbstractVarInfo

Abstract supertype for data structures that capture random variables when executing a probabilistic model and accumulate log densities such as the log likelihood or the log joint probability of the model.

See also: VarInfo

struct DefaultContext <: AbstractContext end

The DefaultContext is used by default to compute log the joint probability of the data and parameters when running the model.

IsLeaf

Specifies that the context is a leaf in the context-tree.

IsParent

Specifies that the context is a parent in the context-tree.

struct LikelihoodContext{Tvars} <: AbstractContext
    vars::Tvars
end

The LikelihoodContext enables the computation of the log likelihood of the parameters when running the model. vars can be used to evaluate the log likelihood for specific values of the model's parameters. If vars is nothing, the parameter values inside the VarInfo will be used by default.

The Metadata struct stores some metadata about the parameters of the model. This helps query certain information about a variable, such as its distribution, which samplers sample this variable, its value and whether this value is transformed to real space or not.

Let md be an instance of Metadata:

• md.vns is the vector of all VarName instances.
• md.idcs is the dictionary that maps each VarName instance to its index in

md.vns, md.ranges md.dists, md.orders and md.flags.

• md.vns[md.idcs[vn]] == vn.
• md.dists[md.idcs[vn]] is the distribution of vn.
• md.gids[md.idcs[vn]] is the set of algorithms used to sample vn. This is used in

the Gibbs sampling process.

• md.orders[md.idcs[vn]] is the number of observe statements before vn is sampled.
• md.ranges[md.idcs[vn]] is the index range of vn in md.vals.
• md.vals[md.ranges[md.idcs[vn]]] is the vector of values of corresponding to vn.
• md.flags is a dictionary of true/false flags. md.flags[flag][md.idcs[vn]] is the

value of flag corresponding to vn.

To make md::Metadata type stable, all the md.vns must have the same symbol and distribution type. However, one can have a Julia variable, say x, that is a matrix or a hierarchical array sampled in partitions, e.g. x[1][:] ~ MvNormal(zeros(2), I); x[2][:] ~ MvNormal(ones(2), I), and is managed by a single md::Metadata so long as all the distributions on the RHS of ~ are of the same type. Type unstable Metadata will still work but will have inferior performance. When sampling, the first iteration uses a type unstable Metadata for all the variables then a specialized Metadata is used for each symbol along with a function barrier to make the rest of the sampling type stable.

Metadata()

Construct an empty type unstable instance of Metadata.

struct MiniBatchContext{Tctx, T} <: AbstractContext
    context::Tctx
    loglike_scalar::T
end

The MiniBatchContext enables the computation of log(prior) + s * log(likelihood of a batch) when running the model, where s is the loglike_scalar field, typically equal to the number of data points / batch size. This is useful in batch-based stochastic gradient descent algorithms to be optimizing log(prior) + log(likelihood of all the data points) in the expectation.

struct Model{F,argnames,defaultnames,missings,Targs,Tdefaults}
    name::Symbol
    f::F
    args::NamedTuple{argnames,Targs}
    defaults::NamedTuple{defaultnames,Tdefaults}
end

A Model struct with model evaluation function of type F, arguments of names argnames types Targs, default arguments of names defaultnames with types Tdefaults, and missing arguments missings.

Here argnames, defaultargnames, and missings are tuples of symbols, e.g. (:a, :b).

An argument with a type of Missing will be in missings by default. However, in non-traditional use-cases missings can be defined differently. All variables in missings are treated as random variables rather than observations.

The default arguments are used internally when constructing instances of the same model with different arguments.

Examples

julia> Model(f, (x = 1.0, y = 2.0))
Model{typeof(f),(:x, :y),(),(),Tuple{Float64,Float64},Tuple{}}(f, (x = 1.0, y = 2.0), NamedTuple())

julia> Model(f, (x = 1.0, y = 2.0), (x = 42,))
Model{typeof(f),(:x, :y),(:x,),(),Tuple{Float64,Float64},Tuple{Int64}}(f, (x = 1.0, y = 2.0), (x = 42,))

julia> Model{(:y,)}(f, (x = 1.0, y = 2.0), (x = 42,)) # with special definition of missings
Model{typeof(f),(:x, :y),(:x,),(:y,),Tuple{Float64,Float64},Tuple{Int64}}(f, (x = 1.0, y = 2.0), (x = 42,))

(model::Model)([rng, varinfo, sampler, context])

Sample from the model using the sampler with random number generator rng and the context, and store the sample and log joint probability in varinfo.

The method resets the log joint probability of varinfo and increases the evaluation number of sampler.

Model{missings}(name::Symbol, f, args::NamedTuple, defaults::NamedTuple)

Create a model of name name with evaluation function f and missing arguments overwritten by missings.

Model(name::Symbol, f, args::NamedTuple[, defaults::NamedTuple = ()])

Create a model of name name with evaluation function f and missing arguments deduced from args.

Default arguments defaults are used internally when constructing instances of the same model with different arguments.

A named distribution that carries the name of the random variable with it.

NodeTrait(context)
NodeTrait(f, context)

Specifies the role of context in the context-tree.

The officially supported traits are:

• IsLeaf: context does not have any decendants.
• IsParent: context has a child context to which we often defer. Expects the following methods to be implemented:
  • childcontext
  • setchildcontext

PrefixContext{Prefix}(context)

Create a context that allows you to use the wrapped context when running the model and adds the Prefix to all parameters.

This context is useful in nested models to ensure that the names of the parameters are unique.

See also: @submodel

struct PriorContext{Tvars} <: AbstractContext
    vars::Tvars
end

The PriorContext enables the computation of the log prior of the parameters vars when running the model.

SampleFromPrior

Sampling algorithm that samples unobserved random variables from their prior distribution.

SampleFromUniform

Sampling algorithm that samples unobserved random variables from a uniform distribution.

References

Stan reference manual

Sampler{T}

Generic sampler type for inference algorithms of type T in DynamicPPL.

Sampler should implement the AbstractMCMC interface, and in particular AbstractMCMC.step. A default implementation of the initial sampling step is provided that supports resuming sampling from a previous state and setting initial parameter values. It requires to overload loadstate and initialstep for loading previous states and actually performing the initial sampling step, respectively. Additionally, sometimes one might want to implement initialsampler that specifies how the initial parameter values are sampled if they are not provided. By default, values are sampled from the prior.

SamplingContext(
        [rng::Random.AbstractRNG=Random.GLOBAL_RNG],
        [sampler::AbstractSampler=SampleFromPrior()],
        [context::AbstractContext=DefaultContext()],
)

Create a context that allows you to sample parameters with the sampler when running the model. The context determines how the returned log density is computed when running the model.

See also: DefaultContext, LikelihoodContext, PriorContext

SimpleVarInfo{NT,T} <: AbstractVarInfo

A simple wrapper of the parameters with a logp field for accumulation of the logdensity.

Currently only implemented for NT<:NamedTuple and NT<:Dict.

Notes

The major differences between this and TypedVarInfo are:

1. SimpleVarInfo does not require linearization.
2. SimpleVarInfo can use more efficient bijectors.
3. SimpleVarInfo is only type-stable if NT<:NamedTuple and either a) no indexing is used in tilde-statements, or b) the values have been specified with the correct shapes.

Examples

General usage

julia> using StableRNGs

julia> @model function demo()
           m ~ Normal()
           x = Vector{Float64}(undef, 2)
           for i in eachindex(x)
               x[i] ~ Normal()
           end
           return x
       end
demo (generic function with 2 methods)

julia> m = demo();

julia> rng = StableRNG(42);

julia> ### Sampling ###
       ctx = SamplingContext(rng, SampleFromPrior(), DefaultContext());

julia> # In the `NamedTuple` version we need to provide the place-holder values for
       # the variables which are using "containers", e.g. `Array`.
       # In this case, this means that we need to specify `x` but not `m`.
       _, vi = DynamicPPL.evaluate!!(m, SimpleVarInfo((x = ones(2), )), ctx);

julia> # (✓) Vroom, vroom! FAST!!!
       vi[@varname(x[1])]
0.4471218424633827

julia> # We can also access arbitrary varnames pointing to `x`, e.g.
       vi[@varname(x)]
2-element Vector{Float64}:
 0.4471218424633827
 1.3736306979834252

julia> vi[@varname(x[1:2])]
2-element Vector{Float64}:
 0.4471218424633827
 1.3736306979834252

julia> # (×) If we don't provide the container...
       _, vi = DynamicPPL.evaluate!!(m, SimpleVarInfo(), ctx); vi
ERROR: type NamedTuple has no field x
[...]

julia> # If one does not know the varnames, we can use a `Dict` instead.
       _, vi = DynamicPPL.evaluate!!(m, SimpleVarInfo{Float64}(Dict()), ctx);

julia> # (✓) Sort of fast, but only possible at runtime.
       vi[@varname(x[1])]
-1.019202452456547

julia> # In addtion, we can only access varnames as they appear in the model!
       vi[@varname(x)]
ERROR: KeyError: key x not found
[...]

julia> vi[@varname(x[1:2])]
ERROR: KeyError: key x[1:2] not found
[...]

Indexing

Using NamedTuple as underlying storage.

julia> svi_nt = SimpleVarInfo((m = (a = [1.0], ), ));

julia> svi_nt[@varname(m)]
(a = [1.0],)

julia> svi_nt[@varname(m.a)]
1-element Vector{Float64}:
 1.0

julia> svi_nt[@varname(m.a[1])]
1.0

julia> svi_nt[@varname(m.a[2])]
ERROR: BoundsError: attempt to access 1-element Vector{Float64} at index [2]
[...]

julia> svi_nt[@varname(m.b)]
ERROR: type NamedTuple has no field b
[...]

Using Dict as underlying storage.

julia> svi_dict = SimpleVarInfo(Dict(@varname(m) => (a = [1.0], )));

julia> svi_dict[@varname(m)]
(a = [1.0],)

julia> svi_dict[@varname(m.a)]
1-element Vector{Float64}:
 1.0

julia> svi_dict[@varname(m.a[1])]
1.0

julia> svi_dict[@varname(m.a[2])]
ERROR: BoundsError: attempt to access 1-element Vector{Float64} at index [2]
[...]

julia> svi_dict[@varname(m.b)]
ERROR: type NamedTuple has no field b
[...]

ThreadSafeVarInfo

A ThreadSafeVarInfo object wraps an AbstractVarInfo object and an array of log probabilities for thread-safe execution of a probabilistic model.

TypedVarInfo(vi::UntypedVarInfo)

This function finds all the unique syms from the instances of VarName{sym} found in vi.metadata.vns. It then extracts the metadata associated with each symbol from the global vi.metadata field. Finally, a new VarInfo is created with a new metadata as a NamedTuple mapping from symbols to type-stable Metadata instances, one for each symbol.

struct VarInfo{Tmeta, Tlogp} <: AbstractVarInfo
    metadata::Tmeta
    logp::Base.RefValue{Tlogp}
    num_produce::Base.RefValue{Int}
end

A light wrapper over one or more instances of Metadata. Let vi be an instance of VarInfo. If vi isa VarInfo{<:Metadata}, then only one Metadata instance is used for all the sybmols. VarInfo{<:Metadata} is aliased UntypedVarInfo. If vi isa VarInfo{<:NamedTuple}, then vi.metadata is a NamedTuple that maps each symbol used on the LHS of ~ in the model to its Metadata instance. The latter allows for the type specialization of vi after the first sampling iteration when all the symbols have been observed. VarInfo{<:NamedTuple} is aliased TypedVarInfo.

Note: It is the user's responsibility to ensure that each "symbol" is visited at least once whenever the model is called, regardless of any stochastic branching. Each symbol refers to a Julia variable and can be a hierarchical array of many random variables, e.g. x[1] ~ ... and x[2] ~ ... both have the same symbol x.

condition(model::Model; values...)
condition(model::Model, values::NamedTuple)

Return a Model which now treats the variables in values as observations.

See also: decondition, conditioned

Limitations

This does currently not work with variables that are provided to the model as arguments, e.g. @model function demo(x) ... end means that condition will not affect the variable x.

Therefore if one wants to make use of condition and decondition one should not be specifying any random variables as arguments.

This is done for the sake of backwards compatibility.

Examples

Simple univariate model

julia> using Distributions; using StableRNGs; rng = StableRNG(42); # For reproducibility.

julia> @model function demo()
           m ~ Normal()
           x ~ Normal(m, 1)
           return (; m=m, x=x)
       end
demo (generic function with 2 methods)

julia> model = demo();

julia> model(rng)
(m = -0.6702516921145671, x = -0.22312984965118443)

julia> # Create a new instance which treats `x` as observed
       # with value `100.0`, and similarly for `m=1.0`.
       conditioned_model = condition(model, x=100.0, m=1.0);

julia> conditioned_model(rng)
(m = 1.0, x = 100.0)

julia> # Let's only condition on `x = 100.0`.
       conditioned_model = condition(model, x = 100.0);

julia> conditioned_model(rng)
(m = 1.3736306979834252, x = 100.0)

julia> # We can also use the nicer `|` syntax.
       conditioned_model = model | (x = 100.0, );

julia> conditioned_model(rng)
(m = 1.3095394956381083, x = 100.0)

Condition only a part of a multivariate variable

Not only can be condition on multivariate random variables, but we can also use the standard mechanism of setting something to missing in the call to condition to only condition on a part of the variable.

julia> @model function demo_mv(::Type{TV}=Float64) where {TV}
           m = Vector{TV}(undef, 2)
           m[1] ~ Normal()
           m[2] ~ Normal()
           return m
       end
demo_mv (generic function with 3 methods)

julia> model = demo_mv();

julia> conditioned_model = condition(model, m = [missing, 1.0]);

julia> conditioned_model(rng) # (✓) `m[1]` sampled, `m[2]` is fixed
2-element Vector{Float64}:
 0.12607002180931043
 1.0

Intuitively one might also expect to be able to write model | (x[1] = 1.0, ). Unfortunately this is not supported due to performance.

julia> condition(model, var"x[2]" = 1.0)(rng) # (×) `x[2]` is not set to 1.0.
2-element Vector{Float64}:
  0.683947930996541
 -1.019202452456547

We will likely provide some syntactic sugar for this in the future.

Nested models

condition of course also supports the use of nested models through the use of @submodel.

julia> @model demo_inner() = m ~ Normal()
demo_inner (generic function with 2 methods)

julia> @model function demo_outer()
           @submodel m = demo_inner()
           return m
       end
demo_outer (generic function with 2 methods)

julia> model = demo_outer();

julia> model(rng)
-0.7935128416361353

julia> conditioned_model = model | (m = 1.0, );

julia> conditioned_model(rng)
1.0

But one needs to be careful when prefixing variables in the nested models:

julia> @model function demo_outer_prefix()
           @submodel prefix="inner" m = demo_inner()
           return m
       end
demo_outer_prefix (generic function with 2 methods)

julia> # This doesn't work now!
       conditioned_model = demo_outer_prefix() | (m = 1.0, );

julia> conditioned_model(rng)
1.7747246334368165

julia> # `m` in `demo_inner` is referred to as `inner.m` internally, so we do:
       conditioned_model = demo_outer_prefix() | (var"inner.m" = 1.0, );

julia> conditioned_model(rng)
1.0

julia> # Note that the above `var"..."` is just standard Julia syntax:
       keys((var"inner.m" = 1.0, ))
(Symbol("inner.m"),)

The difference is maybe more obvious once we look at how these different in their trace/VarInfo:

julia> keys(VarInfo(demo_outer()))
1-element Vector{VarName{:m, Tuple{}}}:
 m

julia> keys(VarInfo(demo_outer_prefix()))
1-element Vector{VarName{Symbol("inner.m"), Tuple{}}}:
 inner.m

From this we can tell what the correct way to condition m within demo_inner is in the two different models.

condition([context::AbstractContext,] values::NamedTuple)
condition([context::AbstractContext]; values...)

Return ConditionContext with values and context if values is non-empty, otherwise return context which is DefaultContext by default.

See also: decondition

decondition(context::AbstractContext, syms...)

Return context but with syms no longer conditioned on.

Note that this recursively traverses contexts, deconditioning all along the way.

See also: condition

decondition(model::Model)
decondition(model::Model, syms...)

Return a Model for which syms... are not considered observations. If no syms are provided, then all variables currently considered observations will no longer be.

This is essentially the inverse of condition. This also means that it suffers from the same limitiations.

Examples

julia> using Distributions; using StableRNGs; rng = StableRNG(42); # For reproducibility.

julia> @model function demo()
           m ~ Normal()
           x ~ Normal(m, 1)
           return (; m=m, x=x)
       end
demo (generic function with 2 methods)

julia> conditioned_model = condition(demo(), m = 1.0, x = 10.0);

julia> conditioned_model(rng)
(m = 1.0, x = 10.0)

julia> model = decondition(conditioned_model, :m);

julia> model(rng)
(m = -0.6702516921145671, x = 10.0)

julia> # `decondition` multiple at once:
       decondition(model, :m, :x)(rng)
(m = 0.4471218424633827, x = 1.820752540446808)

julia> # `decondition` without any symbols will `decondition` all variables.
       decondition(model)(rng)
(m = 1.3095394956381083, x = 1.4356095174474188)

julia> # Usage of `Val` to perform `decondition` at compile-time if possible
       # is also supported.
       model = decondition(conditioned_model, Val{:m}());

julia> model(rng)
(m = 0.683947930996541, x = 10.0)

evaluate!!(model::Model[, rng, varinfo, sampler, context])

Sample from the model using the sampler with random number generator rng and the context, and store the sample and log joint probability in varinfo.

Returns both the return-value of the original model, and the resulting varinfo.

The method resets the log joint probability of varinfo and increases the evaluation number of sampler.

empty!!(vi::VarInfo)

Empty the fields of vi.metadata and reset vi.logp[] and vi.num_produce[] to zeros.

This is useful when using a sampling algorithm that assumes an empty vi, e.g. SMC.

push!!(vi::VarInfo, vn::VarName, r, dist::Distribution, gid::Selector)

Push a new random variable vn with a sampled value r sampled with a sampler of selector gid from a distribution dist to VarInfo vi.

push!!(vi::VarInfo, vn::VarName, r, dist::Distribution, spl::AbstractSampler)

Push a new random variable vn with a sampled value r sampled with a sampler spl from a distribution dist to VarInfo vi, if it makes sense.

The sampler is passed here to invalidate its cache where defined.

push!!(vi::VarInfo, vn::VarName, r, dist::Distribution)

Push a new random variable vn with a sampled value r from a distribution dist to the VarInfo vi, mutating if it makes sense.

model | (x = 1.0, ...)

Return a Model which now treats variables on the right-hand side as observations.

See condition for more information and examples.

empty!(meta::Metadata)

Empty the fields of meta.

This is useful when using a sampling algorithm that assumes an empty meta, e.g. SMC.

getindex(vi::VarInfo, spl::Union{SampleFromPrior, Sampler})

Return the current value(s) of the random variables sampled by spl in vi.

The value(s) may or may not be transformed to Euclidean space.

getindex(vi::VarInfo, vn::VarName)
getindex(vi::VarInfo, vns::Vector{<:VarName})

Return the current value(s) of vn (vns) in vi in the support of its (their) distribution(s).

If the value(s) is (are) transformed to the Euclidean space, it is (they are) transformed back.

haskey(vi::VarInfo, vn::VarName)

Check whether vn has been sampled in vi.

isempty(vi::VarInfo)

Return true if vi is empty and false otherwise.

keys(vi::SimpleVarInfo)

Return an iterator of keys present in vi.

keys(vi::AbstractVarInfo)

Return an iterator over all vns in vi.

nameof(model::Model)

Get the name of the model as Symbol.

rand([rng=Random.GLOBAL_RNG], [T=NamedTuple], model::Model)

Generate a sample of type T from the prior distribution of the model.

setindex!(vi::VarInfo, val, spl::Union{SampleFromPrior, Sampler})

Set the current value(s) of the random variables sampled by spl in vi to val.

The value(s) may or may not be transformed to Euclidean space.

setindex!(vi::VarInfo, val, vn::VarName)

Set the current value(s) of the random variable vn in vi to val.

The value(s) may or may not be transformed to Euclidean space.

_apply!(kernel!, vi::AbstractVarInfo, values, keys)

Calls kernel!(vi, vn, values, keys) for every vn in vi.

_evaluate!!(model::Model, varinfo, context)

Evaluate the model with the arguments matching the given context and varinfo object.

acclogp!!(vi::VarInfo, logp)

Add logp to the value of the log of the joint probability of the observed data and parameters sampled in vi, mutating if it makes sense.

build_model_info(input_expr)

Builds the model_info dictionary from the model's expression.

build_output(modelinfo, linenumbernode)

Builds the output expression.

canview(lens, container)

Return true if lens can be used to view container, and false otherwise.

Examples

julia> canview(@lens(_.a), (a = 1.0, ))
true

julia> canview(@lens(_.a), (b = 1.0, )) # property `a` does not exist
false

julia> canview(@lens(_.a[1]), (a = [1.0, 2.0], ))
true

julia> canview(@lens(_.a[3]), (a = [1.0, 2.0], )) # out of bounds
false

check_tilde_rhs(x)

Check if the right-hand side x of a ~ is a Distribution or an array of Distributions, then return x.

childcontext(context)

Return the descendant context of context.

conditioned(context::AbstractContext)

Return NamedTuple of values that are conditioned on under context`.

Note that this will recursively traverse the context stack and return a merged version of the condition values.

conditioned(model::Model)

Return NamedTuple of values that are conditioned on under model.

Examples

julia> using Distributions

julia> using DynamicPPL: conditioned, contextualize

julia> @model function demo()
           m ~ Normal()
           x ~ Normal(m, 1)
       end
demo (generic function with 2 methods)

julia> m = demo();

julia> # Returns all the variables we have conditioned on + their values.
       conditioned(condition(m, x=100.0, m=1.0))
(x = 100.0, m = 1.0)

julia> # Nested ones also work (note that `PrefixContext` does nothing to the result).
       cm = condition(contextualize(m, PrefixContext{:a}(condition(m=1.0))), x=100.0);

julia> conditioned(cm)
(x = 100.0, m = 1.0)

julia> # Since we conditioned on `m`, not `a.m` as it will appear after prefixed,
       # `a.m` is treated as a random variable.
       keys(VarInfo(cm))
1-element Vector{VarName{Symbol("a.m"), Tuple{}}}:
 a.m

julia> # If we instead condition on `a.m`, `m` in the model will be considered an observation.
       cm = condition(contextualize(m, PrefixContext{:a}(condition(var"a.m"=1.0))), x=100.0);

julia> conditioned(cm)
(x = 100.0, a.m = 1.0)

julia> keys(VarInfo(cm)) # <= no variables are sampled
Any[]

contextual_isassumption(context, vn)

Return true if vn is considered an assumption by context.

The default implementation for AbstractContext always returns true.

dot_tilde_assume!!(context, right, left, vn, vi)

Handle broadcasted assumed variables, e.g., x .~ MvNormal() (where x does not occur in the model inputs), accumulate the log probability, and return the sampled value and updated vi.

Falls back to dot_tilde_assume(context, right, left, vn, vi).

dot_tilde_assume(context::SamplingContext, right, left, vn, vi)

Handle broadcasted assumed variables, e.g., x .~ MvNormal() (where x does not occur in the model inputs), accumulate the log probability, and return the sampled value for a context associated with a sampler.

Falls back to

dot_tilde_assume(context.rng, context.context, context.sampler, right, left, vn, vi)

dot_tilde_observe!!(context, right, left, vi)

Handle broadcasted observed constants, e.g., [1.0] .~ MvNormal(), accumulate the log probability, and return the observed value and updated vi.

Falls back to dot_tilde_observe(context, right, left, vi).

dot_tilde_observe!!(context, right, left, vname, vi)

Handle broadcasted observed values, e.g., x .~ MvNormal() (where x does occur in the model inputs), accumulate the log probability, and return the observed value and updated vi.

Falls back to dot_tilde_observe!!(context, right, left, vi) ignoring the information about variable name and indices; if needed, these can be accessed through this function, though.

dot_tilde_observe(context::SamplingContext, right, left, vi)

Handle broadcasted observed constants, e.g., [1.0] .~ MvNormal(), accumulate the log probability, and return the observed value for a context associated with a sampler.

Falls back to dot_tilde_observe(context.context, context.sampler, right, left, vi).

evaluate_threadsafe!!(model, varinfo, context)

Evaluate the model with varinfo wrapped inside a ThreadSafeVarInfo.

With the wrapper, Julia's multithreading can be used for observe statements in the model but parallel sampling will lead to undefined behaviour. This method is not exposed and supposed to be used only internally in DynamicPPL.

See also: evaluate_threadunsafe!!

evaluate_threadunsafe!!(model, varinfo, context)

Evaluate the model without wrapping varinfo inside a ThreadSafeVarInfo.

If the model makes use of Julia's multithreading this will lead to undefined behaviour. This method is not exposed and supposed to be used only internally in DynamicPPL.

See also: evaluate_threadsafe!!

generate_dot_tilde(left, right)

Generate the expression that replaces left .~ right in the model body.

generate_mainbody(mod, expr, warn)

Generate the body of the main evaluation function from expression expr and arguments args.

If warn is true, a warning is displayed if internal variables are used in the model definition.

generate_tilde(left, right)

Generate an observe expression for data variables and assume expression for parameter variables.

generated_quantities(model::Model, chain::AbstractChains)

Execute model for each of the samples in chain and return an array of the values returned by the model for each sample.

Examples

General

Often you might have additional quantities computed inside the model that you want to inspect, e.g.

@model function demo(x)
    # sample and observe
    θ ~ Prior()
    x ~ Likelihood()
    return interesting_quantity(θ, x)
end
m = demo(data)
chain = sample(m, alg, n)
# To inspect the `interesting_quantity(θ, x)` where `θ` is replaced by samples
# from the posterior/`chain`:
generated_quantities(m, chain) # <= results in a `Vector` of returned values
                               #    from `interesting_quantity(θ, x)`

Concrete (and simple)

julia> using DynamicPPL, Turing

julia> @model function demo(xs)
           s ~ InverseGamma(2, 3)
           m_shifted ~ Normal(10, √s)
           m = m_shifted - 10

           for i in eachindex(xs)
               xs[i] ~ Normal(m, √s)
           end

           return (m, )
       end
demo (generic function with 1 method)

julia> model = demo(randn(10));

julia> chain = sample(model, MH(), 10);

julia> generated_quantities(model, chain)
10×1 Array{Tuple{Float64},2}:
 (2.1964758025119338,)
 (2.1964758025119338,)
 (0.09270081916291417,)
 (0.09270081916291417,)
 (0.09270081916291417,)
 (0.09270081916291417,)
 (0.09270081916291417,)
 (0.043088571494005024,)
 (-0.16489786710222099,)
 (-0.16489786710222099,)

generated_quantities(model::Model, parameters::NamedTuple)
generated_quantities(model::Model, values, keys)
generated_quantities(model::Model, values, keys)

Execute model with variables keys set to values and return the values returned by the model.

If a NamedTuple is given, keys=keys(parameters) and values=values(parameters).

Example

julia> using DynamicPPL, Distributions

julia> @model function demo(xs)
           s ~ InverseGamma(2, 3)
           m_shifted ~ Normal(10, √s)
           m = m_shifted - 10
           for i in eachindex(xs)
               xs[i] ~ Normal(m, √s)
           end
           return (m, )
       end
demo (generic function with 2 methods)

julia> model = demo(randn(10));

julia> parameters = (; s = 1.0, m_shifted=10);

julia> generated_quantities(model, parameters)
(0.0,)

julia> generated_quantities(model, values(parameters), keys(parameters))
(0.0,)

get_matching_type(spl::AbstractSampler, vi, ::Type{T}) where {T}

Get the specialized version of type T for sampler spl.

For example, if T === Float64 and spl::Hamiltonian, the matching type is eltype(vi[spl]).

get_num_produce(vi::VarInfo)

Return the num_produce of vi.

getall(vi::VarInfo)

Return the values of all the variables in vi.

The values may or may not be transformed to Euclidean space.

getargnames(model::Model)

Get a tuple of the argument names of the model.

getargs_assignment(x)

Return the arguments L and R, if x is an expression of the form L = R, or nothing otherwise.

getargs_dottilde(x)

Return the arguments L and R, if x is an expression of the form L .~ R or (~).(L, R), or nothing otherwise.

getargs_tilde(x)

Return the arguments L and R, if x is an expression of the form L ~ R, or nothing otherwise.

getdist(vi::VarInfo, vn::VarName)

Return the distribution from which vn was sampled in vi.

getgid(vi::VarInfo, vn::VarName)

Return the set of sampler selectors associated with vn in vi.

getidx(vi::VarInfo, vn::VarName)

Return the index of vn in the metadata of vi corresponding to vn.

getlogp(vi::VarInfo)

Return the log of the joint probability of the observed data and parameters sampled in vi.

getmetadata(vi::VarInfo, vn::VarName)

Return the metadata in vi that belongs to vn.

getmissings(model::Model)

Get a tuple of the names of the missing arguments of the model.

getrange(vi::VarInfo, vn::VarName)

Return the index range of vn in the metadata of vi.

getranges(vi::AbstractVarInfo, vns::Vector{<:VarName})

Return the indices of vns in the metadata of vi corresponding to vn.

getval(vi::VarInfo, vns::Vector{<:VarName})

Return the value(s) of vns.

The values may or may not be transformed to Euclidean space.

getval(vi::UntypedVarInfo, vview::Union{Int, UnitRange, Vector{Int}})

Return a view vi.vals[vview].

getval(vi::VarInfo, vn::VarName)

Return the value(s) of vn.

The values may or may not be transformed to Euclidean space.

getvalue(context, vn)

Return value of vn in context.

getvalue_nested(context, vn)

Return the value of the parameter corresponding to vn from context or its descendants.

This is contrast to getvalue which only returns the value vn in context, not recursively looking into its descendants.

hasvalue(context, vn)

Return true if vn is found in context.

hasvalue_nested(context, vn)

Return true if vn is found in context or any of its descendants.

This is contrast to hasvalue which only checks for vn in context, not recursively checking if vn is in any of its descendants.

inargnames(varname::VarName, model::Model)

Statically check whether the variable of name varname is an argument of the model.

Possibly existing indices of varname are neglected.

increment_num_produce!(vi::VarInfo)

Add 1 to num_produce in vi.

initialsampler(sampler::Sampler)

Return the sampler that is used for generating the initial parameters when sampling with sampler.

By default, it returns an instance of SampleFromPrior.

initialstep(rng, model, sampler, varinfo; kwargs...)

Perform the initial sampling step of the sampler for the model.

The varinfo contains the initial samples, which can be provided by the user or sampled randomly.

inmissings(varname::VarName, model::Model)

Statically check whether the variable of name varname is a statically declared unobserved variable of the model.

Possibly existing indices of varname are neglected.

invlink!(vi::VarInfo, spl::AbstractSampler)

Transform the values of the random variables sampled by spl in vi from the Euclidean space back to the support of their distributions and sets their corresponding "trans" flag values to false.

is_flagged(vi::VarInfo, vn::VarName, flag::String)

Check whether vn has a true value for flag in vi.

isassumption(expr[, vn])

Return an expression that can be evaluated to check if expr is an assumption in the model.

Let expr be :(x[1]). It is an assumption in the following cases: 1. x is not among the input data to the model, 2. x is among the input data to the model but with a value missing, or 3. x is among the input data to the model with a value other than missing, but x[1] === missing.

When expr is not an expression or symbol (i.e., a literal), this expands to false.

If vn is specified, it will be assumed to refer to a expression which evaluates to a VarName, and this will be used in the subsequent checks. If vn is not specified, AbstractPPL.drop_escape(varname(expr)) will be used in its place.

isfuncdef(expr)

Return true if expr is any form of function definition, and false otherwise.

islinked(vi::VarInfo, spl::Union{Sampler, SampleFromPrior})

Check whether vi is in the transformed space for a particular sampler spl.

Turing's Hamiltonian samplers use the link and invlink functions from Bijectors.jl to map a constrained variable (for example, one bounded to the space [0, 1]) from its constrained space to the set of real numbers. islinked checks if the number is in the constrained space or the real space.

isliteral(expr)

Return true if expr is a literal, e.g. 1.0 or [1.0, ], and false otherwise.

istrans(vi::VarInfo, vn::VarName)

Return true if vn's values in vi are transformed to Euclidean space, and false if they are in the support of vn's distribution.

leafcontext(context)

Return the leaf of context, i.e. the first descendant context that IsLeaf.

link!(vi::VarInfo, spl::Sampler)

Transform the values of the random variables sampled by spl in vi from the support of their distributions to the Euclidean space and set their corresponding "trans" flag values to true.

loadstate(data)

Load sampler state from data.

logjoint(model::Model, varinfo::AbstractVarInfo)

Return the log joint probability of variables varinfo for the probabilistic model.

See logjoint and loglikelihood.

logjoint(model::Model, θ)

Return the log joint probability of variables θ for the probabilistic model.

See logjoint and loglikelihood.

Examples

julia> @model function demo(x)
           m ~ Normal()
           for i in eachindex(x)
               x[i] ~ Normal(m, 1.0)
           end
       end
demo (generic function with 2 methods)

julia> # Using a `NamedTuple`.
       logjoint(demo([1.0]), (m = 100.0, ))
-9902.33787706641

julia> # Using a `Dict`.
       logjoint(demo([1.0]), Dict(@varname(m) => 100.0))
-9902.33787706641

julia> # Truth.
       logpdf(Normal(100.0, 1.0), 1.0) + logpdf(Normal(), 100.0)
-9902.33787706641

logprior(model::Model, varinfo::AbstractVarInfo)

Return the log prior probability of variables varinfo for the probabilistic model.

See also logjoint and loglikelihood.

logprior(model::Model, θ)

Return the log prior probability of variables θ for the probabilistic model.

See also logjoint and loglikelihood.

Examples

julia> @model function demo(x)
           m ~ Normal()
           for i in eachindex(x)
               x[i] ~ Normal(m, 1.0)
           end
       end
demo (generic function with 2 methods)

julia> # Using a `NamedTuple`.
       logprior(demo([1.0]), (m = 100.0, ))
-5000.918938533205

julia> # Using a `Dict`.
       logprior(demo([1.0]), Dict(@varname(m) => 100.0))
-5000.918938533205

julia> # Truth.
       logpdf(Normal(), 100.0)
-5000.918938533205

matchingvalue(sampler, vi, value)
matchingvalue(context::AbstractContext, vi, value)

Convert the value to the correct type for the sampler or context and the vi object.

For a context that is not a SamplingContext, we fall back to matchingvalue(SampleFromPrior(), vi, value).

observations(model::Model)

Alias for conditioned.

parent(lens::Setfield.Lens)

Return the parent lens. If lens doesn't have a parent, nothing is returned.

See also: [parent_and_child].

Examples

julia> parent(@lens(_.a[1]))
(@lens _.a)

julia> # Parent of lens without parents results in `nothing`.
       (parent ∘ parent)(@lens(_.a[1])) === nothing
true

parent(vn::VarName)

Return the parent VarName.

Examples

julia> parent(@varname(x.a[1]))
x.a

julia> (parent ∘ parent)(@varname(x.a[1]))
x

julia> (parent ∘ parent ∘ parent)(@varname(x.a[1]))
x

parent_and_child(lens::Setfield.Lens)

Return a 2-tuple of lenses (parent, child) where parent is the parent lens of lens and child is the child lens of lens.

If lens does not have a parent, we return (nothing, lens).

See also: [parent].

Examples

julia> parent_and_child(@lens(_.a[1]))
((@lens _.a), (@lens _[1]))

julia> parent_and_child(@lens(_.a))
(nothing, (@lens _.a))

pointwise_loglikelihoods(model::Model, chain::Chains, keytype = String)

Runs model on each sample in chain returning a Dict{String, Matrix{Float64}} with keys corresponding to symbols of the observations, and values being matrices of shape (num_chains, num_samples).

keytype specifies what the type of the keys used in the returned Dict are. Currently, only String and VarName are supported.

Notes

Say y is a Vector of n i.i.d. Normal(μ, σ) variables, with μ and σ both being <:Real. Then the observe (i.e. when the left-hand side is an observation) statements can be implemented in three ways:

1. using a for loop:

for i in eachindex(y)
    y[i] ~ Normal(μ, σ)
end

1. using .~:

y .~ Normal(μ, σ)

1. using MvNormal:

y ~ MvNormal(fill(μ, n), σ^2 * I)

In (1) and (2), y will be treated as a collection of n i.i.d. 1-dimensional variables, while in (3) y will be treated as a single n-dimensional observation.

This is important to keep in mind, in particular if the computation is used for downstream computations.

Examples

From chain

julia> using DynamicPPL, Turing

julia> @model function demo(xs, y)
           s ~ InverseGamma(2, 3)
           m ~ Normal(0, √s)
           for i in eachindex(xs)
               xs[i] ~ Normal(m, √s)
           end

           y ~ Normal(m, √s)
       end
demo (generic function with 1 method)

julia> model = demo(randn(3), randn());

julia> chain = sample(model, MH(), 10);

julia> pointwise_loglikelihoods(model, chain)
Dict{String,Array{Float64,2}} with 4 entries:
  "xs[3]" => [-1.42862; -2.67573; … ; -1.66251; -1.66251]
  "xs[1]" => [-1.42932; -2.68123; … ; -1.66333; -1.66333]
  "xs[2]" => [-1.6724; -0.861339; … ; -1.62359; -1.62359]
  "y"     => [-1.51265; -0.914129; … ; -1.5499; -1.5499]

julia> pointwise_loglikelihoods(model, chain, String)
Dict{String,Array{Float64,2}} with 4 entries:
  "xs[3]" => [-1.42862; -2.67573; … ; -1.66251; -1.66251]
  "xs[1]" => [-1.42932; -2.68123; … ; -1.66333; -1.66333]
  "xs[2]" => [-1.6724; -0.861339; … ; -1.62359; -1.62359]
  "y"     => [-1.51265; -0.914129; … ; -1.5499; -1.5499]

julia> pointwise_loglikelihoods(model, chain, VarName)
Dict{VarName,Array{Float64,2}} with 4 entries:
  xs[2] => [-1.6724; -0.861339; … ; -1.62359; -1.62359]
  y     => [-1.51265; -0.914129; … ; -1.5499; -1.5499]
  xs[1] => [-1.42932; -2.68123; … ; -1.66333; -1.66333]
  xs[3] => [-1.42862; -2.67573; … ; -1.66251; -1.66251]

Broadcasting

Note that x .~ Dist() will treat x as a collection of independent observations rather than as a single observation.

julia> @model function demo(x)
           x .~ Normal()
       end;

julia> m = demo([1.0, ]);

julia> ℓ = pointwise_loglikelihoods(m, VarInfo(m)); first(ℓ[@varname(x[1])])
-1.4189385332046727

julia> m = demo([1.0; 1.0]);

julia> ℓ = pointwise_loglikelihoods(m, VarInfo(m)); first.((ℓ[@varname(x[1])], ℓ[@varname(x[2])]))
(-1.4189385332046727, -1.4189385332046727)

replace_returns(expr)

Return Expr with all return ... statements replaced with return ..., DynamicPPL.return_values(__varinfo__).

Note that this method will not replace return statements within function definitions. This is checked using isfuncdef.

reset_num_produce!(vi::AbstractVarInfo)

Reset the value of num_produce the log of the joint probability of the observed data and parameters sampled in vi to 0.

resetlogp!!(vi::AbstractVarInfo)

Reset the value of the log of the joint probability of the observed data and parameters sampled in vi to 0, mutating if it makes sense.

set_flag!(vi::VarInfo, vn::VarName, flag::String)

Set vn's value for flag to true in vi.

set_num_produce!(vi::VarInfo, n::Int)

Set the num_produce field of vi to n.

set_retained_vns_del_by_spl!(vi::VarInfo, spl::Sampler)

Set the "del" flag of variables in vi with order > vi.num_produce[] to true.

setall!(vi::VarInfo, val)

Set the values of all the variables in vi to val.

The values may or may not be transformed to Euclidean space.

setchildcontext(parent::AbstractContext, child::AbstractContext)

Reconstruct parent but now using child is its childcontext, effectively updating the child context.

Examples

julia> ctx = SamplingContext();

julia> DynamicPPL.childcontext(ctx)
DefaultContext()

julia> ctx_prior = DynamicPPL.setchildcontext(ctx, PriorContext()); # only compute the logprior

julia> DynamicPPL.childcontext(ctx_prior)
PriorContext{Nothing}(nothing)

setgid!(vi::VarInfo, gid::Selector, vn::VarName)

Add gid to the set of sampler selectors associated with vn in vi.

setleafcontext(left, right)

Return left but now with its leaf context replaced by right.

Note that this also works even if right is not a leaf context, in which case effectively append right to left, dropping the original leaf context of left.

Examples

julia> using DynamicPPL: leafcontext, setleafcontext, childcontext, setchildcontext, AbstractContext

julia> struct ParentContext{C} <: AbstractContext
           context::C
       end

julia> DynamicPPL.NodeTrait(::ParentContext) = DynamicPPL.IsParent()

julia> DynamicPPL.childcontext(context::ParentContext) = context.context

julia> DynamicPPL.setchildcontext(::ParentContext, child) = ParentContext(child)

julia> Base.show(io::IO, c::ParentContext) = print(io, "ParentContext(", childcontext(c), ")")

julia> ctx = ParentContext(ParentContext(DefaultContext()))
ParentContext(ParentContext(DefaultContext()))

julia> # Replace the leaf context with another leaf.
       leafcontext(setleafcontext(ctx, PriorContext()))
PriorContext{Nothing}(nothing)

julia> # Append another parent context.
       setleafcontext(ctx, ParentContext(DefaultContext()))
ParentContext(ParentContext(ParentContext(DefaultContext())))

setlogp!!(vi::VarInfo, logp)

Set the log of the joint probability of the observed data and parameters sampled in vi to logp, mutating if it makes sense.

setorder!(vi::VarInfo, vn::VarName, index::Int)

Set the order of vn in vi to index, where order is the number of observe statements run before samplingvn`.

settrans!(vi::VarInfo, trans::Bool, vn::VarName)

Set the trans flag value of vn in vi.

setval!(vi::AbstractVarInfo, x)
setval!(vi::AbstractVarInfo, values, keys)
setval!(vi::AbstractVarInfo, chains::AbstractChains, sample_idx::Int, chain_idx::Int)

Set the values in vi to the provided values and leave those which are not present in x or chains unchanged.

Notes

This is rather limited for two reasons:

1. It uses subsumes_string(string(vn), map(string, keys)) under the hood, and therefore suffers from the same limitations as subsumes_string.
2. It will set every vn present in keys. It will NOT however set every k present in keys. This means that if vn == [m[1], m[2]], representing some variable m, calling setval!(vi, (m = [1.0, 2.0])) will be a no-op since it will try to find m[1] and m[2] in keys((m = [1.0, 2.0])).

Example

julia> using DynamicPPL, Distributions, StableRNGs

julia> @model function demo(x)
           m ~ Normal()
           for i in eachindex(x)
               x[i] ~ Normal(m, 1)
           end
       end;

julia> rng = StableRNG(42);

julia> m = demo([missing]);

julia> var_info = DynamicPPL.VarInfo(rng, m);

julia> var_info[@varname(m)]
-0.6702516921145671

julia> var_info[@varname(x[1])]
-0.22312984965118443

julia> DynamicPPL.setval!(var_info, (m = 100.0, )); # set `m` and and keep `x[1]`

julia> var_info[@varname(m)] # [✓] changed
100.0

julia> var_info[@varname(x[1])] # [✓] unchanged
-0.22312984965118443

julia> m(rng, var_info); # rerun model

julia> var_info[@varname(m)] # [✓] unchanged
100.0

julia> var_info[@varname(x[1])] # [✓] unchanged
-0.22312984965118443

setval!(vi::UntypedVarInfo, val, vview::Union{Int, UnitRange, Vector{Int}})

Set the value of vi.vals[vview] to val.

setval!(vi::VarInfo, val, vn::VarName)

Set the value(s) of vn in the metadata of vi to val.

The values may or may not be transformed to Euclidean space.

setval_and_resample!(vi::AbstractVarInfo, x)
setval_and_resample!(vi::AbstractVarInfo, values, keys)
setval_and_resample!(vi::AbstractVarInfo, chains::AbstractChains, sample_idx, chain_idx)

Set the values in vi to the provided values and those which are not present in x or chains to be resampled.

Note that this does not resample the values not provided! It will call setflag!(vi, vn, "del") for variables vn for which no values are provided, which means that the next time we call model(vi) these variables will be resampled.

Note

• This suffers from the same limitations as setval!. See setval! for more info.

Example

julia> using DynamicPPL, Distributions, StableRNGs

julia> @model function demo(x)
           m ~ Normal()
           for i in eachindex(x)
               x[i] ~ Normal(m, 1)
           end
       end;

julia> rng = StableRNG(42);

julia> m = demo([missing]);

julia> var_info = DynamicPPL.VarInfo(rng, m);

julia> var_info[@varname(m)]
-0.6702516921145671

julia> var_info[@varname(x[1])]
-0.22312984965118443

julia> DynamicPPL.setval_and_resample!(var_info, (m = 100.0, )); # set `m` and ready `x[1]` for resampling

julia> var_info[@varname(m)] # [✓] changed
100.0

julia> var_info[@varname(x[1])] # [✓] unchanged
-0.22312984965118443

julia> m(rng, var_info); # sample `x[1]` conditioned on `m = 100.0`

julia> var_info[@varname(m)] # [✓] unchanged
100.0

julia> var_info[@varname(x[1])] # [✓] changed
101.37363069798343

See also

• setval!

splitlens(condition, lens)

Return a 3-tuple (parent, child, issuccess) where, if issuccess is true, parent is a lens such that condition(parent) is true and parent ∘ child == lens.

If issuccess is false, then no such split could be found.

Examples

julia> p, c, issucesss = splitlens(@lens(_.a[1])) do parent
           # Succeeds!
           parent == @lens(_.a)
       end
((@lens _.a), (@lens _[1]), true)

julia> p ∘ c
(@lens _.a[1])

julia> splitlens(@lens(_.a[1])) do parent
           # Fails!
           parent == @lens(_.b)
       end
(nothing, (@lens _.a[1]), false)

subsumes_string(u::String, v::String[, u_indexing])

Check whether stringified variable name v describes a sub-range of stringified variable u.

This is a very restricted version subumes(u::VarName, v::VarName) only really supporting:

• Scalar: x subsumes x[1, 2], x[1, 2] subsumes x[1, 2][3], etc.

Note

• To get same matching capabilities as AbstractPPL.subumes(u::VarName, v::VarName) for strings, one can always do eval(varname(Meta.parse(u)) to get VarName of u, and similarly to v. But this is slow.

syms(vi::VarInfo)

Returns a tuple of the unique symbols of random variables sampled in vi.

tilde_assume!!(context, right, vn, vi)

Handle assumed variables, e.g., x ~ Normal() (where x does occur in the model inputs), accumulate the log probability, and return the sampled value and updated vi.

By default, calls tilde_assume(context, right, vn, vi) and accumulates the log probability of vi with the returned value.

tilde_assume(context::SamplingContext, right, vn, vi)

Handle assumed variables, e.g., x ~ Normal() (where x does occur in the model inputs), accumulate the log probability, and return the sampled value with a context associated with a sampler.

Falls back to

tilde_assume(context.rng, context.context, context.sampler, right, vn, vi)

tilde_observe(context, right, left, vi)

Handle observed constants, e.g., 1.0 ~ Normal(), accumulate the log probability, and return the observed value.

By default, calls tilde_observe(context, right, left, vi) and accumulates the log probability of vi with the returned value.

tilde_observe!!(context, right, left, vname, vi)

Handle observed variables, e.g., x ~ Normal() (where x does occur in the model inputs), accumulate the log probability, and return the observed value and updated vi.

Falls back to tilde_observe!!(context, right, left, vi) ignoring the information about variable name and indices; if needed, these can be accessed through this function, though.

tilde_observe(context::SamplingContext, right, left, vi)

Handle observed constants with a context associated with a sampler.

Falls back to tilde_observe(context.context, context.sampler, right, left, vi).

tonamedtuple(vi::VarInfo)

Convert a vi into a NamedTuple where each variable symbol maps to the values and indexing string of the variable.

For example, a model that had a vector of vector-valued variables x would return

(x = ([1.5, 2.0], [3.0, 1.0], ["x[1]", "x[2]"]), )

unset_flag!(vi::VarInfo, vn::VarName, flag::String)

Set vn's value for flag to false in vi.

unwrap_right_left_vns(right, left, vns)

Return the unwrapped distributions on the right-hand side and values and variable names on the left-hand side of a .~ expression such as x .~ Normal().

This is used mainly to unwrap NamedDist distributions and adjust the indices of the variables.

Example

julia> _, _, vns = DynamicPPL.unwrap_right_left_vns(MvNormal(ones(2), I), randn(2, 2), @varname(x)); vns[end]
x[:,2]

julia> _, _, vns = DynamicPPL.unwrap_right_left_vns(Normal(), randn(1, 2), @varname(x)); vns[end]
x[1,2]

julia> _, _, vns = DynamicPPL.unwrap_right_left_vns(Normal(), randn(1, 2), @varname(x[:])); vns[end]
x[:][1,2]

julia> _, _, vns = DynamicPPL.unwrap_right_left_vns(Normal(), randn(3), @varname(x[1])); vns[end]
x[1][3]

unwrap_right_vn(right, vn)

Return the unwrapped distribution on the right-hand side and variable name on the left-hand side of a ~ expression such as x ~ Normal().

This is used mainly to unwrap NamedDist distributions.

updategid!(vi::VarInfo, vn::VarName, spl::Sampler)

Set vn's gid to Set([spl.selector]), if vn does not have a sampler selector linked and vn's symbol is in the space of spl.

use_threadsafe_eval(context::AbstractContext, varinfo::AbstractVarInfo)

Return true if evaluation of a model using context and varinfo should wrap varinfo in ThreadSafeVarInfo, i.e. threadsafe evaluation, and false otherwise.

values_as(varinfo[, Type])

Return the values/realizations in varinfo as Type, if implemented.

If no Type is provided, return values as stored in varinfo.

values_as(vi::AbstractVarInfo, ::Type{NamedTuple})
values_as(vi::AbstractVarInfo, ::Type{Dict})

Return values in vi as the specified type.

values_as(vi::AbstractVarInfo)

loglikelihood(model::Model, varinfo::AbstractVarInfo)

Return the log likelihood of variables varinfo for the probabilistic model.

See also logjoint and logprior.

loglikelihood(model::Model, θ)

Return the log likelihood of variables θ for the probabilistic model.

See also logjoint and logprior.

Examples

julia> @model function demo(x)
           m ~ Normal()
           for i in eachindex(x)
               x[i] ~ Normal(m, 1.0)
           end
       end
demo (generic function with 2 methods)

julia> # Using a `NamedTuple`.
       loglikelihood(demo([1.0]), (m = 100.0, ))
-4901.418938533205

julia> # Using a `Dict`.
       loglikelihood(demo([1.0]), Dict(@varname(m) => 100.0))
-4901.418938533205

julia> # Truth.
       logpdf(Normal(100.0, 1.0), 1.0)
-4901.418938533205

@addlogprob!(ex)

Add the result of the evaluation of ex to the joint log probability.

@model(expr[, warn = false])

Macro to specify a probabilistic model.

If warn is true, a warning is displayed if internal variable names are used in the model definition.

Examples

Model definition:

@model function model(x, y = 42)
    ...
end

To generate a Model, call model(xvalue) or model(xvalue, yvalue).

@submodel prefix=... model
@submodel prefix=... ... = model

Run a Turing model nested inside of a Turing model and add "prefix." as a prefix to all random variables inside of the model.

Valid expressions for prefix=... are:

• prefix=false: no prefix is used.
• prefix=true: attempt to automatically determine the prefix from the left-hand side ... = model by first converting into a VarName, and then calling Symbol on this.
• prefix=expression: results in the prefix Symbol(expression).

The prefix makes it possible to run the same Turing model multiple times while keeping track of all random variables correctly.

Examples

Example models

julia> @model function demo1(x)
           x ~ Normal()
           return 1 + abs(x)
       end;

julia> @model function demo2(x, y, z)
            @submodel prefix="sub1" a = demo1(x)
            @submodel prefix="sub2" b = demo1(y)
            return z ~ Uniform(-a, b)
       end;

When we sample from the model demo2(missing, missing, 0.4) random variables sub1.x and sub2.x will be sampled:

julia> vi = VarInfo(demo2(missing, missing, 0.4));

julia> @varname(var"sub1.x") in keys(vi)
true

julia> @varname(var"sub2.x") in keys(vi)
true

Variables a and b are not tracked since they can be computed from the random variables sub1.x and sub2.x that were tracked when running demo1:

julia> @varname(a) in keys(vi)
false

julia> @varname(b) in keys(vi)
false

We can check that the log joint probability of the model accumulated in vi is correct:

julia> sub1_x = vi[@varname(var"sub1.x")];

julia> sub2_x = vi[@varname(var"sub2.x")];

julia> logprior = logpdf(Normal(), sub1_x) + logpdf(Normal(), sub2_x);

julia> loglikelihood = logpdf(Uniform(-1 - abs(sub1_x), 1 + abs(sub2_x)), 0.4);

julia> getlogp(vi) ≈ logprior + loglikelihood
true

Different ways of setting the prefix

julia> @model inner() = x ~ Normal()
inner (generic function with 2 methods)

julia> # When `prefix` is unspecified, no prefix is used.
       @model outer() = @submodel a = inner()
outer (generic function with 2 methods)

julia> @varname(x) in keys(VarInfo(outer()))
true

julia> # Explicitely don't use any prefix.
       @model outer() = @submodel prefix=false a = inner()
outer (generic function with 2 methods)

julia> @varname(x) in keys(VarInfo(outer()))
true

julia> # Automatically determined from `a`.
       @model outer() = @submodel prefix=true a = inner()
outer (generic function with 2 methods)

julia> @varname(var"a.x") in keys(VarInfo(outer()))
true

julia> # Using a static string.
       @model outer() = @submodel prefix="my prefix" a = inner()
outer (generic function with 2 methods)

julia> @varname(var"my prefix.x") in keys(VarInfo(outer()))
true

julia> # Using string interpolation.
       @model outer() = @submodel prefix="$(inner().name)" a = inner()
outer (generic function with 2 methods)

julia> @varname(var"inner.x") in keys(VarInfo(outer()))
true

julia> # Or using some arbitrary expression.
       @model outer() = @submodel prefix=1 + 2 a = inner()
outer (generic function with 2 methods)

julia> @varname(var"3.x") in keys(VarInfo(outer()))
true

julia> # (×) Automatic prefixing without a left-hand side expression does not work!
       @model outer() = @submodel prefix=true inner()
ERROR: LoadError: cannot automatically prefix with no left-hand side
[...]

Notes

• The choice prefix=expression means that the prefixing will incur a runtime cost. This is also the case for prefix=true, depending on whether the expression on the the right-hand side of ... = model requires runtime-information or not, e.g. x = model will result in the static prefix x, while x[i] = model will be resolved at runtime.

@submodel model
@submodel ... = model

Run a Turing model nested inside of a Turing model.

Examples

julia> @model function demo1(x)
           x ~ Normal()
           return 1 + abs(x)
       end;

julia> @model function demo2(x, y)
            @submodel a = demo1(x)
            return y ~ Uniform(0, a)
       end;

When we sample from the model demo2(missing, 0.4) random variable x will be sampled:

julia> vi = VarInfo(demo2(missing, 0.4));

julia> @varname(x) in keys(vi)
true

Variable a is not tracked since it can be computed from the random variable x that was tracked when running demo1:

julia> @varname(a) in keys(vi)
false

We can check that the log joint probability of the model accumulated in vi is correct:

julia> x = vi[@varname(x)];

julia> getlogp(vi) ≈ logpdf(Normal(), x) + logpdf(Uniform(0, 1 + abs(x)), 0.4)
true