Hidden Markov Model

This page contains resources about Hidden Markov Models and Markov Models in general.

Subfields and Concepts

 * Markov Models
 * Discrete-time Markov Chain (Discrete-time and Discrete State Space)
 * Discrete-time Harris Chain (Discrete-time and Continuous State Space)
 * Continuous-time Markov Chain / Continuous-time Markov Process / Markov Jump Process
 * Continuous-time Stochastic Process with the Markov property (e.g. Wiener Process)
 * Hidden Markov Model (HMM)
 * Coupled HMM
 * Factorial HMM
 * Autoregressive HMM / Regime Switching Markov Model
 * Hierarchical HMM
 * Hidden Markov Random Field
 * Markov Decision Process
 * Partially Observable Markov Decision Process
 * Hierarchical Markov Models
 * Inference in HMM
 * Baum-Welch Algorithm (Expectation-Maximization)
 * Forward-Backward Algorithm
 * Viterbi Algorithm

Book and Book Chapters

 * Puterman, M. L. (2014). Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons.
 * Murphy, K. P. (2012). "Chapter 17: Markov and hidden Markov models". Machine Learning: A Probabilistic Perspective. MIT Press.
 * Barber, D. (2012). "Chapter 23: Discrete-State Markov Models". Bayesian Reasoning and Machine Learning. Cambridge University Press.
 * Barber, D. (2012). "Chapter 7.5: Markov Decision Process ". Bayesian Reasoning and Machine Learning. Cambridge University Press.
 * Alpaydin, E. (2010). "Chapter 15: Hidden Markov Models". Introduction to machine learning. MIT Press.
 * Koller, D., & Friedman, N. (2009). "Section 6.2.3.1: Hidden Markov Models". Probabilistic Graphical Models. MIT Press.
 * Bishop, C. M. (2006). "Chapter 13: Sequential Data". Pattern Recognition and Machine Learning. Springer.

Software

 * markov - C++

Other Resources

 * Markov Models - Notebook
 * Statistical Inference for Markov and Hidden Markov Models - Notebook