MCMC Configurations

Internal API reference for MCMC configuration dataclasses used by inference dispatchers and backend integrations.

AdjustedMCLMCDynamicConfig dataclass

Bases: BaseMCMCConfig

Dynamic adjusted MCLMC (MHMCHMC) configuration.

This maps to blackjax.adjusted_mclmc_dynamic(...) and uses its top-level API arguments.

Attributes:

Name	Type	Description
step_size	float	Integrator step size.
L_proposal_factor	float	Proposal length scaling factor.
divergence_threshold	float	Energy-difference threshold used to flag divergences.
integration_steps_min	int	Minimum random integration steps per proposal.
integration_steps_max	int	Exclusive upper bound for random integration steps per proposal.

BaseMCMCConfig dataclass

Shared configuration options inherited by all MCMC configs.

You do not instantiate this class directly; use one of the concrete subclasses (NUTSConfig, HMCConfig, SGLDConfig, MALAConfig, AdjustedMCLMCDynamicConfig).

Attributes:

Name	Type	Description
num_samples	int	Number of post-warmup samples to return.
num_warmup	int	Number of warmup/burn-in transitions.
num_chains	int	Number of Markov chains to run in parallel.
mcmc_source	MCMCSource	Backend library used for inference. Supported values are "numpyro" and "blackjax".
init_strategy	callable	NumPyro initialization strategy used when constructing unconstrained initial parameters.

HMCConfig dataclass

Bases: BaseMCMCConfig

Hamiltonian Monte Carlo (HMC) configuration.

Attributes:

Name	Type	Description
step_size	float	Integrator step size used by the leapfrog solver.
num_steps	int	Number of leapfrog steps per HMC proposal.

MALAConfig dataclass

Bases: BaseMCMCConfig

Metropolis-Adjusted Langevin Algorithm (MALA) configuration.

Attributes:

Name	Type	Description
step_size	float	Proposal step size used by blackjax.mala.

NUTSConfig dataclass

Bases: BaseMCMCConfig

No-U-Turn Sampler (NUTS) configuration.

SGLDConfig dataclass

Bases: BaseMCMCConfig

Stochastic Gradient Langevin Dynamics (SGLD) configuration.

SGLD performs first-order Langevin updates using noisy gradients and injected Gaussian noise. In this implementation, gradients are computed on the full dataset (no minibatching), so the method behaves as full-batch Langevin dynamics with an annealed step schedule.

Attributes:

Name	Type	Description
step_size	float	Base learning rate used in the SGLD schedule. This should generally be small.
schedule_power	float	Power in the polynomial decay schedule \(\epsilon_t = \text{step_size} \cdot t^{-\text{schedule_power}}\). Values in (0.5, 1.0] are common for asymptotic convergence.