`ArviZ.from_mcmcchains`

— Function```
from_mcmcchains(posterior::MCMCChains.Chains; kwargs...) -> InferenceData
from_mcmcchains(; kwargs...) -> InferenceData
from_mcmcchains(
posterior::MCMCChains.Chains,
posterior_predictive,
predictions,
log_likelihood;
kwargs...
) -> InferenceData
```

Convert data in an `MCMCChains.Chains`

format into an `InferenceData`

.

Any keyword argument below without an an explicitly annotated type above is allowed, so long as it can be passed to `convert_to_inference_data`

.

**Arguments**

`posterior::MCMCChains.Chains`

: Draws from the posterior

**Keywords**

`posterior_predictive::Any=nothing`

: Draws from the posterior predictive distribution or name(s) of predictive variables in`posterior`

`predictions`

: Out-of-sample predictions for the posterior.`prior`

: Draws from the prior`prior_predictive`

: Draws from the prior predictive distribution or name(s) of predictive variables in`prior`

`observed_data`

: Observed data on which the`posterior`

is conditional. It should only contain data which is modeled as a random variable. Keys are parameter names and values.`constant_data`

: Model constants, data included in the model that are not modeled as random variables. Keys are parameter names.`predictions_constant_data`

: Constants relevant to the model predictions (i.e. new`x`

values in a linear regression).`log_likelihood`

: Pointwise log-likelihood for the data. It is recommended to use this argument as a named tuple whose keys are observed variable names and whose values are log likelihood arrays. Alternatively, provide the name of variable in`posterior`

containing log likelihoods.`library=MCMCChains`

: Name of library that generated the chains`coords`

: Map from named dimension to named indices`dims`

: Map from variable name to names of its dimensions`eltypes`

: Map from variable names to eltypes. This is primarily used to assign discrete eltypes to discrete variables that were stored in`Chains`

as floats.

**Returns**

`InferenceData`

: The data with groups corresponding to the provided data

`ArviZ.from_samplechains`

— Function```
from_samplechains(
posterior=nothing;
prior=nothing,
library=SampleChains,
kwargs...,
) -> InferenceData
```

Convert SampleChains samples to an `InferenceData`

.

Either `posterior`

or `prior`

may be a `SampleChains.AbstractChain`

or `SampleChains.MultiChain`

object.

For descriptions of remaining `kwargs`

, see `from_namedtuple`

.