Search Results for "markov"

IMMpractical implements various Markov chain model-based methods for analysis of DNA sequences. Please cite: Sparks, M.E., Brendel, V. and Dorman, K.S. (2007) Markov model variants for appraisal of coding potential in plant DNA. LNBI. 4463:394-405.

The General Hidden Markov Model Library (GHMM) is a C library with additional Python bindings implementing a wide range of types of Hidden Markov Models and algorithms: discrete, continous emissions, basic training, HMM clustering, HMM mixtures.

NOTE: TransGeneScan is migrated to Github (https://github.com/COL-IU/TransGeneScan). TransGeneScan is no longer maintained in SourceForge. Please find the latest version in Github. TransGeneScan is a gene finding tool for Metatranscriptomic sequences. TransGeneScan incorporates strand-specic hidden states, representing coding sequences in sense and anti-sense strands on transcripts in a Hidden Markov Model similar to the one used in FragGeneScan (http://fraggenescan.sourceforge.net...

Hrothgar is a parallel minimizer and Markov Chain Monte Carlo generator by Andisheh Mahdavi of San Francisco State University. It has been used to solve optimization problems in astrophysics (galaxy cluster mass profiles) as well as in experimental particle physics (hadronic tau decays). It is probably adaptable enough to be applied to your merit function if you can write it in C.

The AK toolkit is another kit for building and use Hidden Markov Models (HMMs). Originally developed for handwritten text recognition (HTR) using Bernoulli HMMs, it also implements diagonal Gaussians and can be used for any other purpose.

ESMERALDA is a development environment for statistical recognizers operating on sequential data (speech, handwriting, biological sequences). It supports continuous density Hidden Markov models, Markov chain (N-gramm) models, and Gaussian mixture models.

Stochastic discrete event system analysis and verification are essential in order to ensure reliability in such systems. However, models that cannot be constructed with an hand-made process need to be learned. Thus, the SDES toolbox proposes an automated solution that is embedded in Matlab to learning and analisis generalized semi-Markov processes.

Software package for building, training and scoring hidden Markov models (HMMs).

NullAllEst is the implementation of a maximum likelihood algorithm to estimate the frequency of a null allele in microsatellite genetic data. A Markov Chain Monte Carlo simulation is used to solve the likelihood function.

This project will show how to implement the Hidden Markov Model approximations of Voice Recognition into embedded and low power systems.

CMATLIB is set of libraries for writing numerical applicatons. It includes support for neural-networks, hidden Markov models, kd-trees, and data smoothing. It may be used from C and Scheme programs.