sequince.posterior ================== .. py:module:: sequince.posterior Attributes ---------- .. autoapisummary:: sequince.posterior.LogProposalLike sequince.posterior.JointLogDensityFn Classes ------- .. autoapisummary:: sequince.posterior.TemperedPosterior Functions --------- .. autoapisummary:: sequince.posterior.draw_additional_samples Module Contents --------------- .. py:data:: LogProposalLike .. py:data:: JointLogDensityFn .. py:class:: TemperedPosterior Bases: :py:obj:`sequince.target.TemperedTarget` Wrapper exposing posterior building blocks for SMC workflows. :param log_prior: Callable returning ``log p(x)`` for each particle. :param log_likelihood: Callable returning ``log l(x)`` for each particle. May be omitted when ``log_likelihood_and_proposal`` is supplied. :param log_proposal: Optional callable or proposal object returning ``log q(x)``. When supplied the intermediate targets follow the bridge ``q(x)^{1-β} [p(x)L(x)]^{β}``. :param log_proposal_override_key: Context metadata key containing a runtime proposal override. :param log_likelihood_and_proposal: Alternative to separate likelihood and proposal inputs: a callable returning ``(log l(x), log q(x))`` from shared computation. Accepts particles and optionally context. Requesting either component evaluates and caches both. A runtime proposal override takes precedence over the returned proposal; refreshing only that override skips the joint call. :param log_density_ratio: Optional direct terminal-to-initial log-density ratio for weighting. Accepts particles and optionally context. When supplied, weighting skips full density preparation; MCMC still evaluates the posterior normally. The callback must account for any runtime proposal override itself. .. py:attribute:: log_prior :type: sequince.target.TargetComponentFn .. py:attribute:: log_likelihood :type: sequince.target.TargetComponentFn | None :value: None .. py:attribute:: log_proposal :type: LogProposalLike | None :value: None .. py:attribute:: log_proposal_override_key :type: str :value: 'log_proposal_override' .. py:attribute:: log_likelihood_and_proposal :type: JointLogDensityFn | None :value: None .. py:attribute:: log_density_ratio :type: sequince.target.TargetComponentFn | None :value: None .. py:method:: evaluation_components(context) Components needed for a full density evaluation, when known. These are the components used to compute ``log_prob``, such as during MCMC. They need not include every component the target supports: ``log_increment`` is included only when the density itself requires it. Other components can still be requested explicitly through ``prepare_evaluation(components=...)``. When ``components=None``, preparation uses this declaration to complete a partial cache and retain only these components. Returning None preserves the default full-evaluation contract for custom targets. .. py:method:: log_increment(particles, context, *, evaluation=None) Return the terminal-to-initial log-density ratio. .. py:method:: log_prob(particles, context, *, evaluation=None) Return the interpolated log density at the current beta. .. py:method:: resolve_log_proposal(log_proposal_override = None) Return the effective proposal callable for the current evaluation. .. py:method:: log_initial(particles, context, *, evaluation = None) Return the proposal or prior endpoint log density. When ``evaluation`` is omitted, all posterior components are evaluated from ``particles``. Supplying a matching evaluation avoids recomputing those components. .. py:method:: log_terminal(particles, context, *, evaluation = None) Return the posterior endpoint log density. When ``evaluation`` is omitted, all posterior components are evaluated from ``particles``. Supplying a matching evaluation avoids recomputing those components. .. py:method:: evaluate(particles, context, *, components = None, evaluation = None) Evaluate or refresh posterior components for a particle population. .. py:method:: freeze_log_prob(context, *, log_proposal_override = None) .. py:function:: draw_additional_samples(*, log_prior = None, log_likelihood = None, log_proposal = None, posterior = None, result, rng, n_particles, mutation = 'minipcn', mutation_n_steps = 10, mutation_kwargs = None, particle_transform = 'affine', particle_transform_kwargs = None) Draw additional samples from the current particles to fill up to n_particles. :param log_prior: Callable evaluating the log prior density ``log p(x)``. If `posterior` is not provided, both `log_prior` and `log_likelihood` must be given. :param log_likelihood: Callable evaluating the log likelihood ``log l(x)``. If `posterior` is not provided, both `log_prior` and `log_likelihood` must be given. :param log_proposal: Optional callable or proposal object for ``log q(x)`` when importance sampling proposals are used. :param posterior: TemperedPosterior object defining the target distribution. :param state: Current ParticleState containing particles and weights. :param context: StepContext providing shared information such as beta. :param rng: Random number generator. :param n_particles: Desired total number of particles after drawing additional samples. :param mutation: Mutation strategy to apply to the new particles. :param mutation_n_steps: Number of mutation steps to apply if mutation is used. :param mutation_kwargs: Additional keyword arguments to pass to the mutation step. :param particle_transform: ParticleTransform instance or string identifier for the transform to use during mutation. If `None`, no transform is applied.