pybop.samplers.base_sampler#

Classes#

Base class for Monte Carlo samplers.

class pybop.samplers.base_sampler.BaseSampler(log_pdf: pybop.LogPosterior | list[pybop.LogPosterior], x0, chains: int, cov0: numpy.ndarray | float)[source]#

Base class for Monte Carlo samplers.

Parameters:

log_pdf (pybop.LogPosterior or List[pybop.LogPosterior]) – The posterior or PDF to be sampled.
chains (int) – Number of chains to be used.
x0 – List-like initial values of the parameters for Monte Carlo sampling.
cov0 – The covariance matrix to be sampled.
Note (Samplers perform maximisation of the Posterior by default.)

abstractmethod run() → numpy.ndarray[source]#

Sample from the posterior distribution.

set_initial_phase_iterations(iterations=250)[source]#

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

Parameters:: iterations (int) – Number of iterations for the initial phase.

set_max_iterations(iterations=500)[source]#

Set the maximum number of iterations for the sampler.

set_parallel(parallel=False)[source]#

Enable or disable parallel evaluation. Credit: PINTS

Parameters:: parallel (bool or int, optional) – If True, use as many worker processes as there are CPU cores. If an integer, use that many workers. If False or 0, disable parallelism (default: False).