liesel.goose

Goose MCMC framework.

Functions

history_to_df(history)	Turns a OptimResult.history dictionary into a pandas.DataFrame.
plot_cor(results[, params, param_indices, ...])	Visualizes autocorrelations of posterior samples.
plot_density(results[, params, ...])	Visualizes posterior distributions with a density plot.
plot_param(results, param[, param_index, ...])	Visualizes trace plot, density plot and autocorrelation plot of a single subparameter.
plot_trace(results[, params, param_indices, ...])	Visualizes posterior samples over time with a trace plot.
plot_scatter(results, params, param_indices)	Produces a scatterplot of two parameters.
plot_pairs(results[, params, param_indices, ...])	Produces a pairplot panel.
stan_epochs([warmup_duration, ...])	Sets up a list of EpochConfig's.
optim_flat(model_train, params[, optimizer, ...])	Optimize the parameters of a Liesel Model.

Classes

DictInterface(log_prob_fn)	A model interface for a model state represented by a dict[str, Array] and a corresponding log-probability function.
DataclassInterface(log_prob_fn)	A model interface for a model state represented by a dataclass and a corresponding log-probability function.
DictModel(log_prob_fn)	Alias for DictInterface, provided for backwards compatibility.
DataClassModel(log_prob_fn)	Alias for DataclassInterface, provided for backwards compatibility.
LieselInterface(model)	A ModelInterface for a Liesel Model.
Engine(seeds, model_states, kernel_sequence, ...)	MCMC engine capable of combining multiple transition kernels.
EngineBuilder(seed, num_chains)	The EngineBuilder is used to construct an MCMC Engine.
EpochConfig(type, duration, thinning, optional)	Defines an Epoch in an MCMC algorithm.
EpochType(value)	Indicates which MCMC phase the epoch is part of.
GibbsKernel(position_keys, transition_fn)	A Gibbs kernel implementing the Kernel protocol.
HMCKernel(position_keys[, ...])	A HMC kernel with dual averaging and an inverse mass matrix tuner, implementing the Kernel protocol.
IWLSKernel(position_keys[, chol_info_fn, ...])	An IWLS kernel with dual averaging and an (optional) user-defined function for computing the Cholesky decomposition of the Fisher information matrix, implementing the liesel.goose.types.Kernel protocol.
MHKernel(position_keys, proposal_fn[, ...])	A Metropolis-Hastings kernel implementing the Kernel protocol.
MHProposal(position, log_correction)
ModelInterface(*args, **kwargs)	Defines a standardized way for Goose to communicate with a statistical model.
NamedTupleInterface(log_prob_fn)	A model interface for a model state represented by a NamedTuple and a corresponding log-probability function.
NUTSKernel(position_keys[, ...])	A NUTS kernel with dual averaging and an inverse mass matrix tuner, implementing the Kernel protocol.
Stopper(max_iter, patience[, atol, rtol])	Handles (early) stopping for optim_flat().
RWKernel(position_keys[, initial_step_size, ...])	A random walk kernel.
Summary(results[, additional_chain, ...])	A summary object.
SamplingResults(positions, transition_infos, ...)	Contains the results of the MCMC engine.
OptimResult(model_state, position, ...)	Holds the results of model optimization with optim_flat().