MH

class cuqi.experimental.mcmc.MH(target=None, proposal=None, scale=1, **kwargs)

Metropolis-Hastings (MH) sampler.

Parameters:

• target (cuqi.density.Density) – Target density or distribution.

• proposal (cuqi.distribution.Distribution or callable) – Proposal distribution. If None, a random walk MH is used (i.e., Gaussian proposal with identity covariance).

• scale (float) – Scaling parameter for the proposal distribution.

• kwargs (dict) – Additional keyword arguments to be passed to the base class ProposalBasedSampler.

__init__(target=None, proposal=None, scale=1, **kwargs)

Initializer for abstract base class for samplers that use a proposal distribution.

Any subclassing samplers should simply store input parameters as part of the __init__ method.

Initialization of the sampler should be done in the _initialize method.

See Sampler for additional details.

Parameters:

• target (cuqi.density.Density) – The target density.

• proposal (cuqi.distribution.Distribution, optional) – The proposal distribution. If not specified, the default proposal is used.

• scale (float, optional) – The scale parameter for the proposal distribution.

• **kwargs (dict) – Additional keyword arguments passed to the Sampler initializer.

Methods

__init__([target, proposal, scale])	Initializer for abstract base class for samplers that use a proposal distribution.
get_history()	Return the history of the sampler.
get_samples()	Return the samples.
get_state()	Return the state of the sampler.
initialize()	Initialize the sampler by setting and allocating the state and history before sampling starts.
load_checkpoint(path)	Load the state of the sampler from a file.
reinitialize()	Re-initialize the sampler.
sample(Ns[, batch_size, sample_path])	Sample Ns samples from the target density.
save_checkpoint(path)	Save the state of the sampler to a file.
set_history(history)	Set the history of the sampler.
set_state(state)	Set the state of the sampler.
step()	Perform one step of the sampler by transitioning the current point to a new point according to the sampler's transition kernel.
tune(skip_len, update_count)	Tune the parameters of the sampler.
validate_proposal()	Validate the proposal distribution.
validate_target()	Validate the target is compatible with the sampler.
warmup(Nb[, tune_freq])	Warmup the sampler by drawing Nb samples.

Attributes

dim	Dimension of the target density.
geometry	Geometry of the target density.
proposal	The proposal distribution.
target	Return the target density.