Hidden+Markov+Model

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>>> **Note**: It explains the HMM from Markov model, which starts with fundamental background of HMM. >>> **Note**: This tutorial includes two documents. Part 1 describes two basic concepts of HMM: Markov model and Gaussian Mixture model. >>> **Note**: This is the part 2 of the HMM tutorial. It explains both discrete and continuous HMMs. >>> **Note**: This paper appears in the journal: //IEICE - Transactions on Information and Systems//, vol. E89-D, no. 3, 2006. It explains the relationship between HMM and graphical models. This paper is somewhat mathematical. >>> **Note**: The web page includes a matlab code. >> >>
 * Hidden Markov Model (HMM) **
 * 1) = **Introduction/Tutorial** =
 * 2) [|HMM in Wikipedia]
 * 3) L. R. Rabiner, "[|A Tutorial on HMM and Selected Applications in Speech Recognition] ," //Proceedings of IEEE,// vol. 7, no. 2, 1989. Note: It is a very classic and important paper to study HMM. Although some concepts in this paper are related to speech recognition, all importatnt terminologies of HMM are introduced in this tutorial paper. It is a **MUST-READ** paper.
 * 4) Book chapters
 * Chapter 15: Hidden Markov Models, //[|Introduction to Machine Learning] //, 2nd, E. Alpaydin, MIT Press, 2010, pp. 363-386.
 * 1) Tutorials (Simple enough for HMM beginners)
 * 2) [|Markov Models and Hidden Markov Models: a brief tutorual], E. Fosler-Lussier, //ICSI Technical Report TR-98-041//, 1998.
 * 1) [|Hidden Markov Models: Fundamentals and Applications, Part 1: Markov Chains and Mixture Models], V. A. Petrushin, //Online Symposium for Eletronics Engineer//, 2000. [File: [[image:http://c1.wikicdn.com/i/mime/32/application/pdf.png height="32" link="http://bn-.wikispaces.com/file/view/3-HMM-Fundamentals+and+Applications-Part1.pdf"]] [|3-HMM-Fundamentals and Applications-Part1.pdf] ]
 * 1) [|Hidden Markov Models: Fundamentals and Applications, Part 2: Discrete and Continuous HMM], V. A. Petrushin, //Online Symposium for Eletronics Engineer//, 2000. [File: [[image:http://c1.wikicdn.com/i/mime/32/application/pdf.png height="32" link="http://bn-.wikispaces.com/file/view/4-HMM-Fundamentals+and+Applications-Part2.pdf"]] [|4-HMM-Fundamentals and Applications-Part2.pdf] ]
 * 1) What HMMs Can Do, J. A. Bilmes, //UWEE Technical Report UWEETR-2002-0003//, 2002. [File: [[image:http://c1.wikicdn.com/i/mime/32/application/pdf.png height="32" link="http://bn-.wikispaces.com/file/view/2-What+HMMs+can+do.pdf"]] [|2-What HMMs can do.pdf], [|Link] ]
 * 1) Hidden Markov Models - A Tutorial for the course Computational Intelligence, B. Resch. File]
 * 1) = **Applications** =
 * 2) Video Recognition
 * 3) Image Recognition
 * 4) Biosignal processing
 * 5) = Algorithhms =
 * 6) == Viterbi ==
 * 7) == Baum-Welch ==
 * 8) = Library / Open Source =
 * 9) [C] GHMM Library
 * The General Hidden Markov Model library (GHMM) is a freely available C library implementing efficient data structures and algorithms for basic and extended HMMs with discrete and continous emissions. It comes with Python wrappers which provide a much nicer interface and added functionality. The GHMM is licensed under the LGPL.
 * Features
 * Discrete and continous emissions
 * Mixtures of PDFs for continous emissions
 * Non-homogenous Markov chains
 * Pair HMMs
 * Clustering and mixture modelling for HMMs
 * Graphical Editor <span style="color: #0044bb; font-family: helvetica,verdana,arial,sans-serif; font-size: 14px; font-weight: 600; text-decoration: none;">[|HMMEd]
 * <span style="color: #222222; font-family: helvetica,verdana,arial,sans-serif; font-size: 15px; line-height: 22px;">Python bindings
 * <span style="color: #222222; font-family: helvetica,verdana,arial,sans-serif; font-size: 15px;">XML-based file format
 * <span style="color: #222222; font-family: helvetica,verdana,arial,sans-serif; font-size: 15px;">Portable (autoconf, automake)
 * Publications
 * <span style="color: #222222; font-family: helvetica,verdana,arial,sans-serif; font-size: 14px; line-height: 17px;">**Michael Seifert** //Analyzing Microarray Data Using Homogenous and Inhomogenous Hidden Markov Models.// Diplomarbeit im Studiengang Bioinformatik, Martin-Luther-Universität Halle (2006)
 * <span style="color: #222222; font-family: helvetica,verdana,arial,sans-serif; font-size: 14px; line-height: 17px;">**A. Schliep, B. Georgi, W. Rungsarityotin, I. G. Costa, A. Schönhuth** //The General Hidden Markov Model Library: Analyzing Systems with Unobservable States//, Proceedings of the Heinz-Billing-Price 2004: 121-136
 * <span style="color: #222222; font-family: helvetica,verdana,arial,sans-serif; font-size: 14px; line-height: 17px;">**B. Knab, A. Schliep, B. Steckemetz and B. Wichern**. Model-Based Clustering With Hidden Markov Models and its Application to Financial Time-Series Data. In //Between Data Science and Applied Data Analysis//, //Springer//, 561–569, 2003.
 * <span style="color: #222222; font-family: helvetica,verdana,arial,sans-serif; font-size: 14px; line-height: 17px;">**B. Georgi.** //A Graph-based Apporach to Clustering of Profile Hidden Markov Models// Bachelor Thesis, FU Berlin.
 * 1) [M] Kevin Murphy's Matlab toolboxes: <span style="background-attachment: initial; background-clip: initial; background-color: initial; background-origin: initial; background-position: 100% 50%; background-repeat: no-repeat no-repeat; cursor: pointer; padding-right: 10px;">[|Hidden Markov models]
 * Very good resource for HMM. Its Matlab codes can support discrete, Gaussian, and Gaussian mixtures PDF for output. Its new version is integrated into PMTK.
 * I will suggest using this toolbox for the test of the GHMM library, because this HMM toolbox provides a document of procedure to test continuous-output HMMs (Gaussian and Gaussian mixtures), including:
 * Generate sequence data of continuous output
 * Training the sequence data
 * Test the sequence data
 * Plot the sequence data
 * 1) [C] <span style="background-attachment: initial; background-clip: initial; background-color: initial; background-origin: initial; background-position: 100% 50%; background-repeat: no-repeat no-repeat; cursor: pointer; padding-right: 10px;">HTK : Hidden Markov Model Toolkit, 3.4.1, 2009. Microsoft.
 * <span style="font-family: sans-serif,ariel; font-size: 16px; line-height: normal;">The Hidden Markov Model Toolkit (HTK) is a portable toolkit for building and manipulating hidden Markov models. HTK is primarily used for speech recognition research although it has been used for numerous other applications including research into speech synthesis, character recognition and DNA sequencing. HTK is in use at hundreds of sites worldwide.
 * <span style="font-family: sans-serif,ariel; font-size: 16px; line-height: normal;">HTK was originally developed at the <span style="background-attachment: initial; background-clip: initial; background-color: initial; background-origin: initial; background-position: 100% 50%; background-repeat: no-repeat no-repeat; color: blue; cursor: pointer; padding-right: 10px; text-decoration: none;">[|Machine Intelligence Laboratory] (formerly known as the Speech Vision and Robotics Group) of the Cambridge University Engineering Department (CUED) where it has been used to build CUED's large vocabulary speech recognition systems (see <span style="background-attachment: initial; background-clip: initial; background-color: initial; background-origin: initial; background-position: 100% 50%; background-repeat: no-repeat no-repeat; color: blue; cursor: pointer; padding-right: 10px; text-decoration: none;">[|CUED HTK LVR] ). In 1993 Entropic Research Laboratory Inc. acquired the rights to sell HTK and the development of HTK was fully transferred to Entropic in 1995 when the Entropic Cambridge Research Laboratory Ltd was established. HTK was sold by Entropic until 1999 when Microsoft bought Entropic. Microsoft has now licensed HTK back to CUED and is providing support so that CUED can redistribute HTK and provide development support via the HTK3 web site. See <span style="background-attachment: initial; background-clip: initial; background-color: initial; background-origin: initial; background-position: 100% 50%; background-repeat: no-repeat no-repeat; color: blue; cursor: pointer; padding-right: 10px; text-decoration: none;">[|History of HTK] for more details.
 * 1) = Books =
 * Hidden Markov Models - Applications in Computer Vision, H. Bunke and T. Caelli Eds., World Scientific, 2001.
 * Hidden Markov and Other Models for Discrete-valued Time Serise, I. L. MacDonald and W. Zucchini, Chapman & Hall, 1997.
 * Hidden Markov Models for Bioinformatics, T. Koski, Kluwer, 2001.
 * Inference in Hidden Markov Models, O. Cappe, E. Moulines, T. Ryden, Springer, 2005.