Module sequential 

Sequential / temporal models (PRML ch 13) — estimators over time-indexed data.

• hmm — discrete Hidden Markov Model (scaled forward, Viterbi, Baum-Welch).

Linear dynamical systems / Kalman filtering land here next (build order in stats_plan.md).

Re-exports

pub use hmm::baum_welch;

pub use hmm::Hmm;

pub use kalman::KalmanFilter;

Modules

hmm

Discrete Hidden Markov Model (PRML ch 13.2) — the standard estimators over a sequence of discrete observations: the scaled forward algorithm for the sequence log-likelihood, Viterbi for the most-likely state path, and Baum-Welch (EM) to learn the parameters. Mission note: time-indexed provenance / life-record reasoning is temporal; this is the canonical model over censored temporal evidence. Kernel-class Reduction (the message passes).

kalman

Kalman filter (PRML ch 13.3) — exact inference for a linear-Gaussian state-space model. Recursively estimates the hidden state x and its covariance P from noisy linear observations. The matrix products reuse linear_algebra::gemm and the innovation-covariance inverse reuses linear_algebra::cholesky (no new solver). Kernel-class DenseLinear.