## Spectral Learning for Mixture of Markov Models (NIPS 2013 Spectral Learning Workshop)

Here is our work with Cem Subakan, Taylan Cemgil and Bulent Sankur. The Paper Supplementary Material Workshop Poster

Recently I’ve been working on learning parameters of a mixture of Dirichlet distributions, I needed a measure to check how good my algorithm works on synthetic data. I was advised to use Kullback-Leibler divergence, but its derivation was a little difficult. Here is the derivation:

Here I present my Matlab implementation for learning Markov mixtures with EM algorithm. David Barber’s book explains the model and the EM derivation very nicely, and also provides a Matlab code package for it. Both the book and codes are open, you’re recommended to have look at them. However, Barber’s code is written for readability… Read More »

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