Julia implementation of hidden Markov models
- Works with continuous or discrete observations
- Customizable emission distributions
- Pre-implemented emission distribution models:
- Discrete (on a finite set {1,...,K})
- Univariate normal
- Multivariate normal with diagonal covariance
- Viterbi algorithm
- Forward algorithm
- Backward algorithm
- Baum-Welch algorithm (expectation-maximization) for parameter estimation
- Generate data from an HMM
If you use this software in your research, please cite:
Jeffrey W. Miller (2016). Lecture Notes on Advanced Stochastic Modeling. Duke University, Durham, NC.
This software was written as part of the "Advanced Stochastic Modeling" STA531 course at Duke University, Spring 2016.
Copyright (c) 2016 Jeffrey W. Miller. This software is released under the MIT License (see LICENSE).