Bayesian variable order Markov models

Christos Dimitrakakis; JMLR W&CP 9:161-168, 2010.

Abstract

We present a simple, effective generalisation of variable order Markov models to full online Bayesian estimation. The mechanism used is close to that employed in context tree weighting. The main contribution is the addition of a prior, conditioned on context, on the Markov order. The resulting construction uses a simple recursion and can be updated efficiently. This allows the model to make predictions using more complex contexts, as more data is acquired, if necessary. In addition, our model can be alternatively seen as a mixture of tree experts. Experimental results show that the predictive model exhibits consistently good performance in a variety of domains.