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GitHub

API

AbstractMCMC defines an interface for sampling Markov chains.

Model

AbstractMCMC.LogDensityModel — Type
LogDensityModel <: AbstractMCMC.AbstractModel

Wrapper around something that implements the LogDensityProblem.jl interface.

Note that this does not implement the LogDensityProblems.jl interface itself, but it simply useful for indicating to the sample and other AbstractMCMC methods that the wrapped object implements the LogDensityProblems.jl interface.

Fields

  • logdensity: The object that implements the LogDensityProblems.jl interface.
source

Sampler

AbstractMCMC.AbstractSampler — Type
AbstractSampler

The AbstractSampler type is intended to be inherited from when implementing a custom sampler. Any persistent state information should be saved in a subtype of AbstractSampler.

When defining a new sampler, you should also overload the function transition_type, which tells the sample function what type of parameter it should expect to receive.

source

Sampling a single chain

StatsBase.sample — Method
sample(
     rng::Random.AbatractRNG=Random.default_rng(),
     model::AbstractModel,
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GitHub
Design
This page explains the default implementations and design choices of AbstractMCMC. It is not intended for users but for developers that want to implement the AbstractMCMC interface for Markov chain Monte Carlo sampling. The user-facing API is explained in API.
Overview
AbstractMCMC provides a default implementation of the user-facing interface described in API. You can completely neglect these and define your own implementation of the interface. However, as described below, in most use cases the default implementation allows you to obtain support of parallel sampling, progress logging, callbacks, iterators, and transducers for free by just defining the sampling step of your inference algorithm, drastically reducing the amount of code you have to write. In general, the docstrings of the functions described below might be helpful if you intend to make use of the default implementations.
Basic structure
The simplified structure for regular sampling (the actual implementation contains some additional error checks and support for progress logging and callbacks) is
StatsBase.sample(
     rng::Random.AbstractRNG,
     model::AbstractMCMC.AbstractModel,
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GitHub
AbstractMCMC.jl
Abstract types and interfaces for Markov chain Monte Carlo methods.
AbstractMCMC defines an interface for sampling and combining Markov chains. It comes with a default sampling algorithm that provides support of progress bars, parallel sampling (multithreaded and multicore), and user-provided callbacks out of the box. Typically developers only have to define the sampling step of their inference method in an iterator-like fashion to make use of this functionality. Additionally, the package defines an iterator and a transducer for sampling Markov chains based on the interface.