biogeme.bayesian_estimation.sampling_strategy module

Defines the strategy for sampling the MCMC based on hardware configuration and user’s preferences.

Michel Bierlaire Mon Oct 27 2025, 17:01:13

class biogeme.bayesian_estimation.sampling_strategy.SamplingConfig(backend, chain_method, cores, target_accept, init, max_treedepth, nuts_kwargs)

Bases: object

Parameters:

• backend (str)

• chain_method (str | None)

• cores (int | None)

• target_accept (float)

• init (str | None)

• max_treedepth (int | None)

• nuts_kwargs (dict[str, Any] | None)

backend: str

chain_method: str | None

cores: int | None

init: str | None

max_treedepth: int | None

nuts_kwargs: dict[str, Any] | None

target_accept: float

biogeme.bayesian_estimation.sampling_strategy.describe_strategies()

Return type:

str

biogeme.bayesian_estimation.sampling_strategy.make_sampling_config(strategy, target_accept)

Create a SamplingConfig from a short strategy string.

Parameters:

• strategy (str) – One of 'automatic', 'numpyro-parallel', 'numpyro-vectorized', or 'pymc'.

• target_accept (float) – Target acceptance rate.

Returns:

A ready-to-use configuration object.

Return type:

SamplingConfig

Raises:

ValueError – If strategy is not one of the allowed values.