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 …

The best sources are a standard text on HMM such as Rabiner's Tutorial on Hidden Markov Models to understand the theory, the publications using the GHMM and the help information, in particular in the comments in the Python wrapper.

This stochastic process we will model with a HMM. Below > is your shell prompt and >>> is the prompt of the Python interpreter and you should type whatever follows the prompt omitting the blank. Alternatively, you can enter the commands in a text file foo.py and execute that text file with python2.3 -i foo.py .

HMMEd: the HMM Editor GQL Pair HMMs Publications Contributors MPI Molecular Genetics. Developers: Alexander Schliep Benjamin Georgi Wasinee Rungsarityotin Ivan G. Costa Janne Grunau Matthias Heinig Christoph Hafemeister

Linux: various distributions are creative about install locations and the scope of what actually is installed. You want the developer/header whatever-they-are-called packages in addition to that. Note that specific distributions also omit part of the Python standard library (e.g., Ubuntu omits the Python profiler for licensing reasons).

HMMs are stochastic models which encode a probability density over sequences of symbols. These symbols can be discrete letters (A,C,G and T for DNA; 1,2,3,4,5,6 for dice), real numbers (weather measurement over time: temperature) or vectors of either or the combination thereof (weather again: temperature, pressure, percipitation).

The General Hidden Markov Model library (GHMM) has been used for published research papers and theses: Michael Seifert Analyzing Microarray Data Using Homogenous and Inhomogenous Hidden Markov Models.

Robust inference of groups in gene expression time-courses using mixtures of HMM. Proceedings of the ISMB 2004. Bioinformatics, Aug 2004; 20 Suppl 1: I283 - I289.

The pair HMM is mainly specified by a graphML based XML file which can be partly created using the HMMEd. The emission probabilities and the alphabets have to be defined manually in the XML file. Applications include probabilistic sequence alignment and comparative genefinding.

HMMEd: Hidden Markov Model editor. HMMEd (the Hidden Markov Model editor) is a graphical application which allows to create and edit Hidden Markov Models. Supported are discrete emissions on arbitrary alphabets; this includes DNA, RNA, and …