Department of Computer Science
 Rutgers University

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HaMMLET: Dynamically compressed Bayesian Hidden Markov Model using Haar Wavelet Shrinkage

HaMMLET is a powerful open-source implementation of a Bayesian Hidden Markov Model. It uses the Haar wavelet transform to dynamically compress the data, which leads to improved speed and convergence of Forward-Backward Gibbs Sampling. It can be used in applications such as CNV detection from aCGH data. The development is hosted at GitHub (http://wiedenhoeft.github.io/HaMMLET/).

Publications

Wiedenhoeft, John and Brugel, Eric and Schliep, Alexander. Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression (2016) [details]

Wiedenhoeft, John and Brugel, Eric and Schliep, Alexander. Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression (2016) [details]