Module probabilistic_models 

Probabilistic models including Bayesian networks, HMMs, Gaussian processes, Dirichlet processes, variational inference, and expectation-maximization.

These models provide foundational probabilistic machinery for physics-informed machine learning, uncertainty quantification, and data-driven modeling.

Structs

BayesianNetwork

A Bayesian Network: directed acyclic graph with conditional probability tables.

BnNode

A node in a Bayesian network.

DirichletProcess

Dirichlet Process mixture model using the Chinese Restaurant Process.

ExpectationMaximization

Expectation-Maximization for Gaussian Mixture Models.

GaussianProcess

Gaussian Process for regression with various kernel functions.

GmmComponent

A Gaussian mixture model component.

HiddenMarkovModel

A Hidden Markov Model with discrete hidden states and Gaussian emissions.

VariationalInference

Mean-field variational inference for a Gaussian mixture model.

Enums

KernelType

Available kernel functions for Gaussian Processes.