API
AbstractMCMC defines an interface for sampling Markov chains.
Model
AbstractMCMC.AbstractModel — TypeAbstractModelAn AbstractModel represents a generic model type that can be used to perform inference.
AbstractMCMC.LogDensityModel — TypeLogDensityModel <: AbstractMCMC.AbstractModelWrapper 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.
Sampler
AbstractMCMC.AbstractSampler — TypeAbstractSamplerThe 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.
Sampling a single chain
StatsBase.sample — Methodsample(
+API · AbstractMCMC
API
AbstractMCMC defines an interface for sampling Markov chains.
Model
AbstractMCMC.AbstractModel — TypeAbstractModel
An AbstractModel represents a generic model type that can be used to perform inference.
sourceAbstractMCMC.LogDensityModel — TypeLogDensityModel <: 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.
sourceSampler
AbstractMCMC.AbstractSampler — TypeAbstractSampler
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.
sourceSampling a single chain
StatsBase.sample — Methodsample(
rng::Random.AbatractRNG=Random.default_rng(),
model::AbstractModel,
sampler::AbstractSampler,
N_or_isdone;
kwargs...,
-)
Sample from the model with the Markov chain Monte Carlo sampler and return the samples.
If N_or_isdone is an Integer, exactly N_or_isdone samples are returned.
Otherwise, sampling is performed until a convergence criterion N_or_isdone returns true. The convergence criterion has to be a function with the signature
isdone(rng, model, sampler, samples, state, iteration; kwargs...)
where state and iteration are the current state and iteration of the sampler, respectively. It should return true when sampling should end, and false otherwise.
Keyword arguments
See https://turinglang.org/AbstractMCMC.jl/dev/api/#Common-keyword-arguments for common keyword arguments.
sourceStatsBase.sample — Methodsample(
+)
Sample from the model with the Markov chain Monte Carlo sampler and return the samples.
If N_or_isdone is an Integer, exactly N_or_isdone samples are returned.
Otherwise, sampling is performed until a convergence criterion N_or_isdone returns true. The convergence criterion has to be a function with the signature
isdone(rng, model, sampler, samples, state, iteration; kwargs...)
where state and iteration are the current state and iteration of the sampler, respectively. It should return true when sampling should end, and false otherwise.
Keyword arguments
See https://turinglang.org/AbstractMCMC.jl/dev/api/#Common-keyword-arguments for common keyword arguments.
sourceStatsBase.sample — Methodsample(
rng::Random.AbstractRNG=Random.default_rng(),
logdensity,
sampler::AbstractSampler,
N_or_isdone;
kwargs...,
-)
Wrap the logdensity function in a LogDensityModel, and call sample with the resulting model instead of logdensity.
The logdensity function has to support the LogDensityProblems.jl interface.
sourceIterator
AbstractMCMC.steps — Methodsteps(
+)
Wrap the logdensity function in a LogDensityModel, and call sample with the resulting model instead of logdensity.
The logdensity function has to support the LogDensityProblems.jl interface.
sourceIterator
AbstractMCMC.steps — Methodsteps(
rng::Random.AbstractRNG=Random.default_rng(),
model::AbstractModel,
sampler::AbstractSampler;
@@ -486,12 +28,12 @@
julia> iterator = steps(MyModel(), MySampler());
julia> collect(Iterators.take(iterator, 10)) == zeros(10)
-true
sourceAbstractMCMC.steps — Methodsteps(
+true
sourceAbstractMCMC.steps — Methodsteps(
rng::Random.AbstractRNG=Random.default_rng(),
logdensity,
sampler::AbstractSampler;
kwargs...,
-)
Wrap the logdensity function in a LogDensityModel, and call steps with the resulting model instead of logdensity.
The logdensity function has to support the LogDensityProblems.jl interface.
sourceTransducer
AbstractMCMC.Sample — MethodSample(
+)
Wrap the logdensity function in a LogDensityModel, and call steps with the resulting model instead of logdensity.
The logdensity function has to support the LogDensityProblems.jl interface.
sourceTransducer
AbstractMCMC.Sample — MethodSample(
rng::Random.AbstractRNG=Random.default_rng(),
model::AbstractModel,
sampler::AbstractSampler;
@@ -508,12 +50,12 @@
julia> transducer = Sample(MyModel(), MySampler());
julia> collect(transducer(1:10)) == zeros(10)
-true
sourceAbstractMCMC.Sample — MethodSample(
+true
sourceAbstractMCMC.Sample — MethodSample(
rng::Random.AbstractRNG=Random.default_rng(),
logdensity,
sampler::AbstractSampler;
kwargs...,
-)
Wrap the logdensity function in a LogDensityModel, and call Sample with the resulting model instead of logdensity.
The logdensity function has to support the LogDensityProblems.jl interface.
sourceSampling multiple chains in parallel
StatsBase.sample — Methodsample(
+)
Wrap the logdensity function in a LogDensityModel, and call Sample with the resulting model instead of logdensity.
The logdensity function has to support the LogDensityProblems.jl interface.
sourceSampling multiple chains in parallel
StatsBase.sample — Methodsample(
rng::Random.AbstractRNG=Random.default_rng(),
model::AbstractModel,
sampler::AbstractSampler,
@@ -521,7 +63,7 @@
N::Integer,
nchains::Integer;
kwargs...,
-)
Sample nchains Monte Carlo Markov chains from the model with the sampler in parallel using the parallel algorithm, and combine them into a single chain.
Keyword arguments
See https://turinglang.org/AbstractMCMC.jl/dev/api/#Common-keyword-arguments for common keyword arguments.
sourceStatsBase.sample — Methodsample(
+)
Sample nchains Monte Carlo Markov chains from the model with the sampler in parallel using the parallel algorithm, and combine them into a single chain.
Keyword arguments
See https://turinglang.org/AbstractMCMC.jl/dev/api/#Common-keyword-arguments for common keyword arguments.
sourceStatsBase.sample — Methodsample(
rng::Random.AbstractRNG=Random.default_rng(),
logdensity,
sampler::AbstractSampler,
@@ -529,7 +71,7 @@
N::Integer,
nchains::Integer;
kwargs...,
-)
Wrap the logdensity function in a LogDensityModel, and call sample with the resulting model instead of logdensity.
The logdensity function has to support the LogDensityProblems.jl interface.
sourceTwo algorithms are provided for parallel sampling with multiple threads and multiple processes, and one allows for the user to sample multiple chains in serial (no parallelization):
AbstractMCMC.MCMCThreads — TypeMCMCThreads
The MCMCThreads algorithm allows users to sample MCMC chains in parallel using multiple threads.
sourceAbstractMCMC.MCMCDistributed — TypeMCMCDistributed
The MCMCDistributed algorithm allows users to sample MCMC chains in parallel using multiple processes.
sourceAbstractMCMC.MCMCSerial — TypeMCMCSerial
The MCMCSerial algorithm allows users to sample serially, with no thread or process parallelism.
sourceCommon keyword arguments
Common keyword arguments for regular and parallel sampling are:
progress (default: AbstractMCMC.PROGRESS[] which is true initially): toggles progress loggingchain_type (default: Any): determines the type of the returned chaincallback (default: nothing): if callback !== nothing, then callback(rng, model, sampler, sample, iteration) is called after every sampling step, where sample is the most recent sample of the Markov chain and iteration is the current iterationnum_warmup (default: 0): number of "warm-up" steps to take before the first "regular" step, i.e. number of times to call AbstractMCMC.step_warmup before the first call to AbstractMCMC.step.discard_initial (default: num_warmup): number of initial samples that are discarded. Note that if discard_initial < num_warmup, warm-up samples will also be included in the resulting samples.thinning (default: 1): factor by which to thin samples.initial_state (default: nothing): if initial_state !== nothing, the first call to AbstractMCMC.step is passed initial_state as the state argument.
Info The common keyword arguments progress, chain_type, and callback are not supported by the iterator AbstractMCMC.steps and the transducer AbstractMCMC.Sample.
There is no "official" way for providing initial parameter values yet. However, multiple packages such as EllipticalSliceSampling.jl and AdvancedMH.jl support an initial_params keyword argument for setting the initial values when sampling a single chain. To ensure that sampling multiple chains "just works" when sampling of a single chain is implemented, we decided to support initial_params in the default implementations of the ensemble methods:
initial_params (default: nothing): if initial_params isa AbstractArray, then the ith element of initial_params is used as initial parameters of the ith chain. If one wants to use the same initial parameters x for every chain, one can specify e.g. initial_params = FillArrays.Fill(x, N).
Progress logging can be enabled and disabled globally with AbstractMCMC.setprogress!(progress).
AbstractMCMC.setprogress! — Functionsetprogress!(progress::Bool; silent::Bool=false)
Enable progress logging globally if progress is true, and disable it otherwise. Optionally disable informational message if silent is true.
sourceChains
The chain_type keyword argument allows to set the type of the returned chain. A common choice is to return chains of type Chains from MCMCChains.jl.
AbstractMCMC defines the abstract type AbstractChains for Markov chains.
AbstractMCMC.AbstractChains — TypeAbstractChains
AbstractChains is an abstract type for an object that stores parameter samples generated through a MCMC process.
sourceFor chains of this type, AbstractMCMC defines the following two methods.
AbstractMCMC.chainscat — Functionchainscat(c::AbstractChains...)
Concatenate multiple chains.
By default, the chains are concatenated along the third dimension by calling cat(c...; dims=3).
sourceAbstractMCMC.chainsstack — Functionchainsstack(c::AbstractVector)
Stack chains in c.
By default, the vector of chains is returned unmodified. If eltype(c) <: AbstractChains, then reduce(chainscat, c) is called.
sourceInteracting with states of samplers
To make it a bit easier to interact with some arbitrary sampler state, we encourage implementations of AbstractSampler to implement the following methods:
AbstractMCMC.getparams — Functiongetparams(state[, logdensity_function])
Retrieve the values of parameters from the sampler's state as a Vector{<:Real}.
sourceAbstractMCMC.setparams!! — Functionsetparams!!(state, params[, logdensity_function])
Set the values of parameters in the sampler's state from a Vector{<:Real}.
This function should follow the BangBang interface: mutate state in-place if possible and return the mutated state. Otherwise, it should return a new state containing the updated parameters.
Although not enforced, it should hold that setparams!!(state, getparams(state)) == state. In other words, the sampler should implement a consistent transformation between its internal representation and the vector representation of the parameter values.
Sometimes, to maintain the consistency of the log density and parameter values, an optional logdensity_function can be provided. This is useful for samplers that need to evaluate the log density at the new parameter values.
sourceThese methods can also be useful for implementing samplers which wraps some inner samplers, e.g. a mixture of samplers.
Example: MixtureSampler
In a MixtureSampler we need two things:
components: collection of samplers.weights: collection of weights representing the probability of choosing the corresponding sampler.
struct MixtureSampler{W,C} <: AbstractMCMC.AbstractSampler
+)
Wrap the logdensity function in a LogDensityModel, and call sample with the resulting model instead of logdensity.
The logdensity function has to support the LogDensityProblems.jl interface.
Two algorithms are provided for parallel sampling with multiple threads and multiple processes, and one allows for the user to sample multiple chains in serial (no parallelization):
AbstractMCMC.MCMCThreads — TypeMCMCThreads
The MCMCThreads algorithm allows users to sample MCMC chains in parallel using multiple threads.
sourceAbstractMCMC.MCMCDistributed — TypeMCMCDistributed
The MCMCDistributed algorithm allows users to sample MCMC chains in parallel using multiple processes.
sourceAbstractMCMC.MCMCSerial — TypeMCMCSerial
The MCMCSerial algorithm allows users to sample serially, with no thread or process parallelism.
sourceCommon keyword arguments
Common keyword arguments for regular and parallel sampling are:
progress (default: AbstractMCMC.PROGRESS[] which is true initially): toggles progress loggingchain_type (default: Any): determines the type of the returned chaincallback (default: nothing): if callback !== nothing, then callback(rng, model, sampler, sample, iteration) is called after every sampling step, where sample is the most recent sample of the Markov chain and iteration is the current iterationnum_warmup (default: 0): number of "warm-up" steps to take before the first "regular" step, i.e. number of times to call AbstractMCMC.step_warmup before the first call to AbstractMCMC.step.discard_initial (default: num_warmup): number of initial samples that are discarded. Note that if discard_initial < num_warmup, warm-up samples will also be included in the resulting samples.thinning (default: 1): factor by which to thin samples.initial_state (default: nothing): if initial_state !== nothing, the first call to AbstractMCMC.step is passed initial_state as the state argument.
Info The common keyword arguments progress, chain_type, and callback are not supported by the iterator AbstractMCMC.steps and the transducer AbstractMCMC.Sample.
There is no "official" way for providing initial parameter values yet. However, multiple packages such as EllipticalSliceSampling.jl and AdvancedMH.jl support an initial_params keyword argument for setting the initial values when sampling a single chain. To ensure that sampling multiple chains "just works" when sampling of a single chain is implemented, we decided to support initial_params in the default implementations of the ensemble methods:
initial_params (default: nothing): if initial_params isa AbstractArray, then the ith element of initial_params is used as initial parameters of the ith chain. If one wants to use the same initial parameters x for every chain, one can specify e.g. initial_params = FillArrays.Fill(x, N).
Progress logging can be enabled and disabled globally with AbstractMCMC.setprogress!(progress).
AbstractMCMC.setprogress! — Functionsetprogress!(progress::Bool; silent::Bool=false)
Enable progress logging globally if progress is true, and disable it otherwise. Optionally disable informational message if silent is true.
sourceChains
The chain_type keyword argument allows to set the type of the returned chain. A common choice is to return chains of type Chains from MCMCChains.jl.
AbstractMCMC defines the abstract type AbstractChains for Markov chains.
AbstractMCMC.AbstractChains — TypeAbstractChains
AbstractChains is an abstract type for an object that stores parameter samples generated through a MCMC process.
sourceFor chains of this type, AbstractMCMC defines the following two methods.
AbstractMCMC.chainscat — Functionchainscat(c::AbstractChains...)
Concatenate multiple chains.
By default, the chains are concatenated along the third dimension by calling cat(c...; dims=3).
sourceAbstractMCMC.chainsstack — Functionchainsstack(c::AbstractVector)
Stack chains in c.
By default, the vector of chains is returned unmodified. If eltype(c) <: AbstractChains, then reduce(chainscat, c) is called.
sourceInteracting with states of samplers
To make it a bit easier to interact with some arbitrary sampler state, we encourage implementations of AbstractSampler to implement the following methods:
AbstractMCMC.getparams — Functiongetparams(state[, logdensity_function])
Retrieve the values of parameters from the sampler's state as a Vector{<:Real}.
sourceAbstractMCMC.setparams!! — Functionsetparams!!(state, params[, logdensity_function])
Set the values of parameters in the sampler's state from a Vector{<:Real}.
This function should follow the BangBang interface: mutate state in-place if possible and return the mutated state. Otherwise, it should return a new state containing the updated parameters.
Although not enforced, it should hold that setparams!!(state, getparams(state)) == state. In other words, the sampler should implement a consistent transformation between its internal representation and the vector representation of the parameter values.
Sometimes, to maintain the consistency of the log density and parameter values, an optional logdensity_function can be provided. This is useful for samplers that need to evaluate the log density at the new parameter values.
sourceThese methods can also be useful for implementing samplers which wraps some inner samplers, e.g. a mixture of samplers.
Example: MixtureSampler
In a MixtureSampler we need two things:
components: collection of samplers.weights: collection of weights representing the probability of choosing the corresponding sampler.
struct MixtureSampler{W,C} <: AbstractMCMC.AbstractSampler
components::C
weights::W
end
To implement the state, we need to keep track of a couple of things:
index: the index of the sampler used in this step.states: the current states of all the components.
We need to keep track of the states of all components rather than just the state for the sampler we used previously. The reason is that lots of samplers keep track of more than just the previous realizations of the variables, e.g. in AdvancedHMC.jl we keep track of the momentum used, the metric used, etc.
struct MixtureState{S}
@@ -607,5 +149,4 @@
transition, state = AbstractMCMC.step(rng, model, sampler)
while ...
transition, state = AbstractMCMC.step(rng, model, sampler, state)
-end
Settings
This document was generated with Documenter.jl version 1.7.0 on Wednesday 23 October 2024. Using Julia version 1.11.1.