Module hmm 

Hidden Markov Models: discrete + Gaussian emissions, forward-backward, Viterbi decoding, and Baum-Welch (EM) parameter learning. Also includes Variational Bayes EM (Dirichlet priors) and Hidden Semi-Markov Models with explicit duration distributions.

Re-exports

pub use baum_welch::BaumWelchGaussianResult;

pub use baum_welch::BaumWelchResult;

pub use baum_welch::baum_welch_discrete;

pub use baum_welch::baum_welch_gaussian;

pub use forward_backward::ForwardBackward;

pub use forward_backward::forward_backward;

pub use hmm::HmmDiscrete;

pub use hmm::HmmGaussian;

pub use hmm::log_safe;

pub use posterior_decoding::PosteriorDecode;

pub use posterior_decoding::posterior_decode;

pub use posterior_decoding::posterior_path_is_feasible;

pub use scaling::ScaledBackwardResult;

pub use scaling::ScaledForwardBackwardResult;

pub use scaling::ScaledForwardResult;

pub use scaling::scaled_backward;

pub use scaling::scaled_baum_welch_step;

pub use scaling::scaled_forward;

pub use scaling::scaled_forward_backward;

pub use scaling::scaled_viterbi;

pub use semimarkov::DurationDistrib;

pub use semimarkov::HsmConfig;

pub use semimarkov::HsmResult;

pub use semimarkov::Hsmm;

pub use semimarkov::hsm_fit;

pub use variational::VbHmmConfig;

pub use variational::VbHmmResult;

pub use variational::digamma;

pub use variational::log_gamma;

pub use variational::variational_hmm;

pub use viterbi::ViterbiResult;

pub use viterbi::viterbi;

Modules

baum_welch

Baum-Welch (EM) parameter learning for discrete and Gaussian HMMs.

forward_backward

Log-space forward-backward for discrete HMMs and Gaussian HMMs.

hmm

HMM model definitions and shared helpers.

posterior_decoding

Posterior (max-marginal) decoding for discrete HMMs.

scaling

semimarkov

Hidden Semi-Markov Model (HSMM) with explicit duration distributions.

variational

Variational Bayes EM for Hidden Markov Models with Dirichlet priors.

viterbi

Log-space Viterbi decoding for discrete HMMs.