Code covered by the BSD License  

Highlights from
Bayesian robust hidden Markov model

Be the first to rate this file! 39 Downloads (last 30 days) File Size: 16.7 KB File ID: #43616
image thumbnail

Bayesian robust hidden Markov model

by

 

25 Sep 2013 (Updated )

MatLab object for segmenting sequences of real-valued data with noise, outliers and missing values.

| Watch this File

File Information
Description

The Bayesian robust hidden Markov model (BRHMM) is a probabilistic model for segmenting sequential multi-variate data. The model explains the data as having been generated by a sequence of hidden states. Each state is a finite mixture of heavy-tailed distributions with with state-specific mixing proportions and shared location/dispersion parameters. All parameters in the model are equipped with conjugate prior distributions and are learnt with a variational Bayesian (vB) inference algorithm similar in spirit to expectation-maximization. The algorithm is robust to outliers and accepts missing values.

This submission includes a test function that generates a set of synthetic data and learns a model from these data. The test function also plots the data segmented according to the model, and the variational lower bound on the log-likelihood of the data after each vB iteration.

If you find this submission useful for your research/work please cite my MathWorks community profile. Feel free to contact me directly if you have any technical or application-related questions.

INSTRUCTIONS:

After downloading this submission, extract the compressed file inside your MatLab working directory and run the test function (TestBRHMM.m) for a demonstration.

Required Products MATLAB
MATLAB release MATLAB 7.14 (R2012a)
Tags for This File   Please login to tag files.
Please login to add a comment or rating.
Comments and Ratings (1)
08 Jul 2014 Ben

Is there a reference paper for this method?

Updates
09 Oct 2013

Minor code improvements.

17 Dec 2013

Minor changes in the code and updates to the documentation.

Contact us