diff --git a/index.html b/index.html index 6a5afc3..3ac2596 100644 --- a/index.html +++ b/index.html @@ -1,2 +1,3 @@ + diff --git a/previews/PR148/api/index.html b/previews/PR148/api/index.html index 2270403..cba4635 100644 --- a/previews/PR148/api/index.html +++ b/previews/PR148/api/index.html @@ -1,5 +1,463 @@ -API · AbstractMCMC
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(
+API · AbstractMCMC
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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,
     sampler::AbstractSampler,
@@ -150,3 +608,4 @@
 while ...
     transition, state = AbstractMCMC.step(rng, model, sampler, state)
 end

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diff --git a/previews/PR148/design/index.html b/previews/PR148/design/index.html
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-Design · AbstractMCMC
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(
+Design · AbstractMCMC
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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,
     sampler::AbstractMCMC.AbstractSampler,
@@ -25,3 +483,4 @@
 
     return AbstractMCMC.bundle_samples(samples, model, sampler, state, chain_type; kwargs...)
 end
All other default implementations make use of the same structure and in particular call the same methods.
Sampling step
The only method for which no default implementation is provided (and hence which downstream packages have to implement) is AbstractMCMC.step. It defines the sampling step of the inference method.
AbstractMCMC.step — Function
step(rng, model, sampler[, state; kwargs...])
Return a 2-tuple of the next sample and the next state of the MCMC sampler for model.
Samples describe the results of a single step of the sampler. As an example, a sample might include a vector of parameters sampled from a prior distribution.
When sampling using sample, every step call after the first has access to the current state of the sampler.
source
If one also has some special handling of the warmup-stage of sampling, then this can be specified by overloading
AbstractMCMC.step_warmup — Function
step_warmup(rng, model, sampler[, state; kwargs...])
Return a 2-tuple of the next sample and the next state of the MCMC sampler for model.
When sampling using sample, this takes the place of AbstractMCMC.step in the first num_warmup number of iterations, as specified by the num_warmup keyword to sample. This is useful if the sampler has an initial "warmup"-stage that is different from the standard iteration.
By default, this simply calls AbstractMCMC.step.
source
which will be used for the first num_warmup iterations, as specified as a keyword argument to AbstractMCMC.sample.  Note that this is optional; by default it simply calls AbstractMCMC.step from above.
Collecting samples
Note
This section does not apply to the iterator and transducer interface.
After the initial sample is obtained, the default implementations for regular and parallel sampling (not for the iterator and the transducer since it is not needed there) create a container for all samples (the initial one and all subsequent samples) using AbstractMCMC.samples.
AbstractMCMC.samples — Function
samples(sample, model, sampler[, N; kwargs...])
Generate a container for the samples of the MCMC sampler for the model, whose first sample is sample.
The method can be called with and without a predefined number N of samples.
source
In each step, the sample is saved in the container by AbstractMCMC.save!!. The notation !! follows the convention of the package BangBang.jl which is used in the default implementation of AbstractMCMC.save!!. It indicates that the sample is pushed to the container but a "widening" fallback is used if the container type does not allow to save the sample. Therefore AbstractMCMC.save!! always has to return the container.
AbstractMCMC.save!! — Function
save!!(samples, sample, iteration, model, sampler[, N; kwargs...])
Save the sample of the MCMC sampler at the current iteration in the container of samples.
The function can be called with and without a predefined number N of samples. By default, AbstractMCMC uses push!! from the Julia package BangBang to append to the container, and widen its type if needed.
source
For most use cases the default implementation of AbstractMCMC.samples and AbstractMCMC.save!! should work out of the box and hence need not to be overloaded in downstream code.
Creating chains
Note
This section does not apply to the iterator and transducer interface.
At the end of the sampling procedure for regular and paralle sampling we transform the collection of samples to the desired output type by calling AbstractMCMC.bundle_samples.
AbstractMCMC.bundle_samples — Function
bundle_samples(samples, model, sampler, state, chain_type[; kwargs...])
Bundle all samples that were sampled from the model with the given sampler in a chain.
The final state of the sampler can be included in the chain. The type of the chain can be specified with the chain_type argument.
By default, this method returns samples.
source
The default implementation should be fine in most use cases, but downstream packages could, e.g., save the final state of the sampler as well if they overload AbstractMCMC.bundle_samples.

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-Home · AbstractMCMC
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.

+Home · AbstractMCMC
+
+
+
+
+
+
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.

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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,
diff --git a/v5.5.0/design/index.html b/v5.5.0/design/index.html
index 4569863..5c9ca79 100644
--- a/v5.5.0/design/index.html
+++ b/v5.5.0/design/index.html
@@ -457,6 +457,7 @@
     });
 
 
+
 
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,
diff --git a/v5.5.0/index.html b/v5.5.0/index.html
index f4ba063..b2f97ec 100644
--- a/v5.5.0/index.html
+++ b/v5.5.0/index.html
@@ -457,5 +457,6 @@
     });
 
 
+
 
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.