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Journal Articles Journal of Machine Learning Research Year : 2010

On Finding Predictors for Arbitrary Families of Processes


The problem is sequence prediction in the following setting. A sequence $x_1,\dots,x_n,\dots$ of discrete-valued observations is generated according to some unknown probabilistic law (measure) $\mu$. After observing each outcome, it is required to give the conditional probabilities of the next observation. The measure $\mu$ belongs to an arbitrary but known class $C$ of stochastic process measures. We are interested in predictors $\rho$ whose conditional probabilities converge (in some sense) to the ``true'' $\mu$-conditional probabilities if any $\mu\in C$ 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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Dates and versions

inria-00442881 , version 1 (23-12-2009)


  • HAL Id : inria-00442881 , version 1
  • ARXIV : 0912.4883


Daniil Ryabko. On Finding Predictors for Arbitrary Families of Processes. Journal of Machine Learning Research, 2010, 11, pp.581-602. ⟨inria-00442881⟩
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