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Bayesian variable order Markov models.

10 years 4 months ago
Bayesian variable order Markov models.
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.
Christos Dimitrakakis
Added 14 Mar 2010
Updated 19 Mar 2010
Type Conference
Year 2010
Where AISTATS
Authors Christos Dimitrakakis
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