Conditional NML universal models
Jorma Rissanen and Teemu Roos
In: Information Theory and Applications Workshop (ITA-07), 29 Jan - 02 Feb 2007, San Diego, CA.
The NML (Normalized Maximum Likelihood) universal model has certain minmax optimal properties but it has two shortcomings: the normalizing coefficient can be evaluated in a closed form only for special model classes, and it does not define a random process so that it cannot be used for prediction. We present a universal conditional NML model, which has minmax optimal properties similar to those of the regular NML model. However, unlike NML, the conditional NML model defines a random process which can be used for prediction. It also admits a recursive evaluation for data compression.