Bayes Estimator (best-must-have tattoo!)

Version 1.0.2 (3.04 KB) by
This class can be used to guess the hidden states that most likely generate N consecutive observations in a Hidden Markov Model.
Updated 11 Feb 2019

This class can be used to guess the hidden states that most likely generate N consecutive observations in a Hidden Markov Model.

The first method is "maxLikelihood" where the number of hidden states is limited to two (due to the computation issue). The second method is "viterbi" which implements Viterbi algorithm to reduce the search space. In this case, the number of hidden states
can be more than two.

EXAMPLE 1 (check the above video at time 20:20)
transitionsProbabiliy = [0.6 0.4; 0.2 0.8];
emissionProbability = [0.6 0.4; 0.2 0.8];
prior = [1/3 2/3].';
observation = [1 0 1];
[selectedPath, probability4EachPath] = Bayes.maxLikelihood(observation, transitionsProbabiliy, emissionProbability, prior);

EXAMPLE 2 (check the above video at time 22:10)
observation = [1 1 0 0 0 1];
[selectedPath, probabilityPaths] = Bayes.viterbi(observation, transitionsProbabiliy, emissionProbability, prior)

Developed By Iman Moazzen, PhD
Affiliate Assistant Professor, Concordia University, Canada
Senior Applied Researcher at PAI Health, Vancouver

True confession:
If I were to get a tattoo, it would be Bayes theorem for sure! I am truly in ah every single time I've seen its application!

Cite As

Iman (2024). Bayes Estimator (best-must-have tattoo!) (https://www.mathworks.com/matlabcentral/fileexchange/70226-bayes-estimator-best-must-have-tattoo), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2018b
Compatible with any release
Platform Compatibility
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