## On Finding Predictors for Arbitrary Families of Processes

** Daniil Ryabko**; 11(17):581−602, 2010.

### Abstract

The problem is sequence prediction in the following setting.
A sequence *x _{1},...,x_{n},...* of discrete-valued observations is generated
according to some unknown probabilistic law (measure) μ. After observing each outcome,
it is required to give the conditional probabilities of the next observation.
The measure μ belongs to an arbitrary but known class

*C*of stochastic process measures. We are interested in predictors ρ whose conditional probabilities converge (in some sense) to the "true" μ-conditional probabilities, if any μ∈

*C*is chosen to generate the sequence. The contribution of this work is in characterizing the families

*C*for which such predictors exist, and in providing a specific and simple form in which to look for a solution. We show that if any predictor works, then there exists a Bayesian predictor, whose prior is discrete, and which works too. We also find several sufficient and necessary conditions for the existence of a predictor, in terms of topological characterizations of the family

*C*, as well as in terms of local behaviour of the measures in

*C*, which in some cases lead to procedures for constructing such predictors.

It should be emphasized that the framework is completely general: the stochastic processes considered are not required to be i.i.d., stationary, or to belong to any parametric or countable family.

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