hidden Markov model

Finite-state Markov chain {hidden Markov model} (HMM) samples distribution.

model

Hidden Markov models are graphical models. Bayesian models are finite-state Markov chains.

purposes

Hidden Markov chains model signal processing, biology, genetics, ecology, image analysis, economics, and network security. Situations compare error-free or non-criminal distribution to error or criminal distribution.

transition

Hidden-distribution Markov chain has initial distribution and time-constant transition matrix.

calculations

Calculations include estimating parameters by recursion {forward-backward recursion, Markov} {forward-backward Gibbs sampler, Markov} {direct Gibbs sampler, Markov}, filling missing data, finding state-space size, preventing switching, assessing validity, and testing convergence by likelihood recursion.

Whole Section in One File

3-Statistics-Distribution-Markov Process

Drawings

Technical Information

Date Modified: 2022.0224