/BP-AR-HMM

This repository is used to discover and model dynamicl behaviors which are shared among several related time series.

Primary LanguageMATLAB

HongminWu -- Additions:

  1. This is based on software originally written by Emily Fox and Erik Sudderth (see below) and adapted for use in an LfD setting.
  2. The main script to run is demos/*_demo.m, please change the correct path in the 'global_variables' file

BP-AR-HMM (beta process autoregressive hidden Markov model) Matlab Software

Copyright (C) 2009, Emily B. Fox and Erik B. Sudderth. (ebfox[at]alum[dot]mit[dot]edu and sudderth[at]cs[dot]brown[dot]edu)

This software package includes several Matlab scripts and auxiliary functions, which implement MCMC sampling algorithms for the model described in the following publication: Sharing Features among Dynamical Systems with Beta Processes E. B. Fox, E. B. Sudderth, M. I. Jordan, and A. S. Willsky Advances in Neural Information Processing Systems, vol. 22, 2010. Please cite this paper in any publications using the BP-AR-HMM package.

See also: Bayesian Nonparametric Learning of Complex Dynamical Phenomena E. B. Fox Ph.D. Thesis, July, 2009.

Package Organization and Documentation

Summary of BP-AR-HMM package contents:

IBPHMMinference.m: Main inference script using the birth-death RJMCMC sampler for unique features. IBPHMMinference_PoissonProp.m: Main inference script using Poisson proposal for unique features. /utilities:
Script runstuff.m with example inputs to main inference script, along with various other functions used to create necessary structures, etc. /relabeler:
Code to perform optimal mapping between true and estimated mode sequences.

Setup and Usage Examples

For an example of sparse feature extraction, see runstuff.m. To use the BP-AR-HMM code, you must first take two steps:

  1. Install Minka's lightspeed toolbox and add directory to path: http://research.microsoft.com/~minka/software/lightspeed/
  2. Add /relabeler and /utilities directory to path

Acknowledgments & Copyright & License

Portions of the package were adapted from Yee Whye Teh's "Nonparametric Bayesian Mixture Models" package, release 1. Available from: http://www.gatsby.ucl.ac.uk/~ywteh

Copyright (C) 2009, Emily B. Fox and Erik B. Sudderth.

http://web.mit.edu/ebfox/www/

Permission is granted for anyone to copy, use, or modify these programs and accompanying documents for purposes of research or education, provided this copyright notice is retained, and note is made of any changes that have been made.

These programs and documents are distributed without any warranty, express or implied. As the programs were written for research purposes only, they have not been tested to the degree that would be advisable in any important application. All use of these programs is entirely at the user's own risk.