Hidden Markov Models: Supplemental material Lecture slides Discrete State HMMs: A. W. Moore, Hidden Markov Models. Slides from a tutorial presentation. L. R. Rabiner (1989), A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Classic reference, with clear descriptions of inference and learning algorithms. Continuous State HMMs: S. Roweis, Z. Ghahramani (1999), A Unifying Review of Linear Gaussian Models. Describes the Kalman filter and other Gaussian models. The Kalman Filter. Wikipedia article describing linear Kalman filtering, as well as nonlinear extensions. M. S. Arulampalam et. al. (2002), A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. Accessible particle filter tutorial with pseudocode for several variants. A. Doucet et. al. (2001), Sequential Monte Carlo Methods in Practice. This edited volume nicely surveys the particle filtering literature. Graphical Models: K. Murphy, A Brief Introduction to Graphical Models and Bayesian Networks. Online graphical model tutorial, with references. M. I. Jordan (2004), Graphical Models. Tutorial introduction to graphical models, inference, and learning. C. M. Bishop (2006), Pattern Recognition and Machine Learning. Recent textbook with a nice chapter on graphical models. Applications and Demonstrations: S. Thrun, W. Burgard, D. Fox (2005), Probabilistic Robotics. Describes applications of HMMs and particle filters in robotics, including result videos. The Condensation Algorithm. Visual tracking using particle filters. Sphinx-4. An open source speech recognition system which employs HMMs. E. Birney (2001), Hidden Markov Models in Biological Sequence Analysis. Applications in bioinformatics. Software: Kevin Murphy's Matlab toolboxes: Hidden Markov models, Kalman filters, and Bayesian networks (directed graphical models). Recursive Bayesian Estimation Library (ReBEL): Matlab code for linear/extended/unscented Kalman filters, and particle filters. R packages: sspir (linear state space models, Kalman filters) and hmm.discnp (discrete-state HMMs with discrete observations). Hidden Markov Model Toolkit (HTK): HMM code distributed as C libraries, focused on speech recognition. General Hidden Markov Model (GHMM) library: Another HMM package written in C.