pybop.samplers.base_sampler

Classes

BaseSampler	Base class for Monte Carlo samplers.
SamplerOptions	Base options for the sampler.

Module Contents

class pybop.samplers.base_sampler.BaseSampler(log_pdf: pybop.problems.problem.Problem, options: SamplerOptions | None = None)

Base class for Monte Carlo samplers.

Parameters:

• log_pdf (pybop.Problem) – The negative unnormalised posterior distribution.

• options (SamplerOptions, optional) – Options for the sampler. If None, default options are used.

static default_options() → SamplerOptions

Get the default options for the sampler.

abstractmethod run() → pybop._result.SamplingResult

Sample from the posterior distribution.

Returns:

Samples from the posterior distribution.

Return type:

np.ndarray

set_initial_phase_iterations(iterations: int = 250)

Set the number of iterations for the initial phase of the sampler.

set_max_iterations(iterations: int = 500)

Set the maximum number of iterations for the sampler.

set_warm_up_iterations(iterations: int = 250)

Set the number of warm up iterations for the sampler.

_log_pdf

_options

_x0

property cov0: numpy.ndarray

property log_pdf: pybop.problems.problem.Problem

property options: SamplerOptions

property x0: numpy.ndarray

class pybop.samplers.base_sampler.SamplerOptions

Base options for the sampler.

n_chains

The number of chains to concurrently sample from.

Type:

int

cov

Covariance matrix.

Type:

float | np.ndarray

validate()

Validate the options.

Raises:

ValueError – If the options are invalid.

cov: float | numpy.ndarray = 0.05

n_chains: int = 1