oxicuda_seq/hmm/
mod.rs

1//! Hidden Markov Models: discrete + Gaussian emissions, forward-backward,
2//! Viterbi decoding, and Baum-Welch (EM) parameter learning.
3//! Also includes Variational Bayes EM (Dirichlet priors) and Hidden
4//! Semi-Markov Models with explicit duration distributions.
5
6pub mod baum_welch;
7pub mod forward_backward;
8pub mod hmm;
9pub mod posterior_decoding;
10pub mod scaling;
11pub mod semimarkov;
12pub mod variational;
13pub mod viterbi;
14
15pub use baum_welch::{
16    BaumWelchGaussianResult, BaumWelchResult, baum_welch_discrete, baum_welch_gaussian,
17};
18pub use forward_backward::{ForwardBackward, forward_backward};
19pub use hmm::{HmmDiscrete, HmmGaussian, log_safe};
20pub use posterior_decoding::{PosteriorDecode, posterior_decode, posterior_path_is_feasible};
21pub use scaling::{
22    ScaledBackwardResult, ScaledForwardBackwardResult, ScaledForwardResult, scaled_backward,
23    scaled_baum_welch_step, scaled_forward, scaled_forward_backward, scaled_viterbi,
24};
25pub use semimarkov::{DurationDistrib, HsmConfig, HsmResult, Hsmm, hsm_fit};
26pub use variational::{VbHmmConfig, VbHmmResult, digamma, log_gamma, variational_hmm};
27pub use viterbi::{ViterbiResult, viterbi};
oxicuda_seq/hmm/mod.rs

oxicuda_seq/hmm/
mod.rs
