https://github.com/tpapp/hiddenmarkovchains.jl

Hidden Markov Chain calculations in Julia
https://github.com/tpapp/hiddenmarkovchains.jl

Last synced: over 1 year ago
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Hidden Markov Chain calculations in Julia

• Host: GitHub
• URL: https://github.com/tpapp/hiddenmarkovchains.jl
• Owner: tpapp
• License: other
• Created: 2016-09-14T12:18:22.000Z (almost 10 years ago)
• Default Branch: master
• Last Pushed: 2020-12-20T09:15:19.000Z (over 5 years ago)
• Last Synced: 2025-02-28T16:20:11.246Z (over 1 year ago)
• Language: Julia
• Size: 26.4 KB
• Stars: 4
• Watchers: 3
• Forks: 1
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

          
# HiddenMarkovChains — a Julia library

[![Project Status: WIP - Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](http://www.repostatus.org/badges/latest/wip.svg)](http://www.repostatus.org/#wip)

[![Build Status](https://travis-ci.org/tpapp/HiddenMarkovChains.jl.svg?branch=master)](https://travis-ci.org/tpapp/HiddenMarkovChains.jl)

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[![codecov.io](http://codecov.io/github/tpapp/HiddenMarkovChains.jl/coverage.svg?branch=master)](http://codecov.io/github/tpapp/HiddenMarkovChains.jl?branch=master)

This is a preliminary collection of functions I use for calculations that involve Hidden Markov Chains (HMC). It is mainly used for

1. [indirect inference](http://www.econ.yale.edu/smith/palgrave7.pdf) for macroeconomic models, in which the state space is discretized. Exact calculation of path probabilities allows smooth functions of model parameters, obviating the need for [explicit smoothing](http://arxiv.org/abs/1507.06115) (of course discretization brings in another set of problems), and

2. likelihood-based methods, such as Bayesian MCMC, with HMCs. The library implements numerically stable calculation of log likelihoods for observed sequences.

*Tamas K. Papp acknowledges support from the Jubiläumsfonds grant (16256) of the Austrian National Bank.*

## Bibliography

*Golub, Gene H., and Carl D. Meyer, Jr.* "Using the QR factorization and group inversion to compute, differentiate, and estimate the sensitivity of stationary probabilities for Markov chains." SIAM Journal on Algebraic Discrete Methods 7.2 (1986): 273-281.

*Rabiner, Lawrence R.* "A tutorial on hidden Markov models and selected applications in speech recognition." Proceedings of the IEEE 77.2 (1989): 257-286.