MCMC Configurations

MCMCInference is configured via MCMC config dataclasses. These specify sampler family, backend source, and algorithm hyperparameters.

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, AdaptiveMetropolisConfig, 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.

NUTSConfig dataclass

Bases: BaseMCMCConfig

No-U-Turn Sampler (NUTS) configuration.

Attributes:

Name	Type	Description
target_acceptance_rate	float	Target acceptance probability used during warmup. Must lie strictly between zero and one.

HMCConfig dataclass

Bases: BaseMCMCConfig

Hamiltonian Monte Carlo (HMC) configuration.

Attributes:

Name	Type	Description
step_size	float	Integrator step size used by the leapfrog solver. Ignored when adapt=True (step size is tuned during warmup).
num_steps	int	Number of leapfrog steps per HMC proposal.
adapt	bool	Whether to tune step size and mass matrix during warmup. Defaults to True. Set to False to use a fixed step_size and identity mass matrix (useful when warmup is expensive or the step size is known in advance).

AdaptiveMetropolisConfig dataclass

Bases: BaseMCMCConfig

Adaptive random-walk Metropolis-within-Gibbs configuration.

One transition updates each flattened unconstrained coordinate in order. Each coordinate update uses a one-dimensional Gaussian random-walk proposal that is accepted or rejected before the next coordinate is visited; the sampler does not make a single joint multivariate-normal proposal. Proposal scales adapt during warmup toward the requested acceptance rate and remain fixed while retained samples are generated.

This sampler is currently implemented by the BlackJAX integration only.

Attributes:

Name	Type	Description
initial_proposal_scale	ArrayLike	Positive scalar proposal scale, or one positive scale per flattened unconstrained coordinate.
target_acceptance_rate	float	Shared target acceptance rate applied to each coordinate update.
adaptation_rate	float	Exponent in the diminishing adaptation step n_iter ** -adaptation_rate.
max_adaptation	float	Maximum change to a log proposal scale in one warmup transition.

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.

MALAConfig dataclass

Bases: BaseMCMCConfig

Metropolis-Adjusted Langevin Algorithm (MALA) configuration.

Attributes:

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