Combining Expert Advice Efficiently
We show how models for prediction with expert advice can be defined concisely and clearly using Hidden Markov Models (HMMs); standard HMM algorithms can then be used to efficiently calculate, among other things, how the expert predictions should be weighted according to the model. We review existing models and shed new light on their relationships. We pay special attention to the Switch-Distribution, which was recently developed to improve Bayesian/Minimum Description Length model selection. We explain how the required calculations can be performed efficiently.