`AdvancedVI.ADVI`

— Type`struct ADVI{AD} <: VariationalInference{AD}`

Automatic Differentiation Variational Inference (ADVI) with automatic differentiation backend `AD`

.

**Fields**

`samples_per_step::Int64`

: Number of samples used to estimate the ELBO in each optimization step.`max_iters::Int64`

: Maximum number of gradient steps.`adtype::Any`

: AD backend used for automatic differentiation.

`AdvancedVI.DecayedADAGrad`

— Type`DecayedADAGrad(η=0.1, pre=1.0, post=0.9)`

Implements a decayed version of AdaGrad. It has parameter specific learning rates based on how frequently it is updated.

**Parameters**

- η: learning rate
- pre: weight of new gradient norm
- post: weight of histroy of gradient norms

```

**References**

ADAGrad optimiser. Parameters don't need tuning.

`AdvancedVI.TruncatedADAGrad`

— Type`TruncatedADAGrad(η=0.1, τ=1.0, n=100)`

Implements a truncated version of AdaGrad in the sense that only the `n`

previous gradient norms are used to compute the scaling rather than *all* previous. It has parameter specific learning rates based on how frequently it is updated.

**Parameters**

- η: learning rate
- τ: constant scale factor
- n: number of previous gradient norms to use in the scaling.

```

**References**

ADAGrad optimiser. Parameters don't need tuning.

TruncatedADAGrad (Appendix E).

`AdvancedVI.grad!`

— Function`grad!(vo, alg::VariationalInference, q, model::Model, θ, out, args...)`

Computes the gradients used in `optimize!`

. Default implementation is provided for `VariationalInference{AD}`

where `AD`

is either `ADTypes.AutoForwardDiff`

or `ADTypes.AutoTracker`

. This implicitly also gives a default implementation of `optimize!`

.

Variance reduction techniques, e.g. control variates, should be implemented in this function.

`AdvancedVI.optimize!`

— Method`optimize!(vo, alg::VariationalInference{AD}, q::VariationalPosterior, model::Model, θ; optimizer = TruncatedADAGrad())`

Iteratively updates parameters by calling `grad!`

and using the given `optimizer`

to compute the steps.

`AdvancedVI.vi`

— Function```
vi(model, alg::VariationalInference)
vi(model, alg::VariationalInference, q::VariationalPosterior)
vi(model, alg::VariationalInference, getq::Function, θ::AbstractArray)
```

Constructs the variational posterior from the `model`

and performs the optimization following the configuration of the given `VariationalInference`

instance.

**Arguments**

`model`

:`Turing.Model`

or`Function`

z ↦ log p(x, z) where`x`

denotes the observations`alg`

: the VI algorithm used`q`

: a`VariationalPosterior`

for which it is assumed a specialized implementation of the variational objective used exists.`getq`

: function taking parameters`θ`

as input and returns a`VariationalPosterior`

`θ`

: only required if`getq`

is used, in which case it is the initial parameters for the variational posterior