We propose a new definition for conditioning in the Chaotic Probability framework. We show that the Conditional Chaotic Probability model that we propose can be given the interpretation of a generalized Markov chain. Chaotic Probabilities were introduced by Fine et al. as an attempt to model chance phenomena with a usual set of measures ${\cal M}$ endowed with an {\em objective, frequentist interpretation} instead of a compound hypothesis or behavioral subjective one. We follow the presentation of the univariate case chaotic probability model and provide an instrumental interpretation of random process measures consistent with a conditional chaotic probability source, which can be used as a tool for simulation of our model. Given a finite time series, we also present a universal method for estimation of conditional chaotic probability models that is based on the analysis of the relative frequencies taken along a set of subsequences chosen by a given set of rules.
Keywords. Imprecise Probabilities, Foundations of Probability, Church Place Selection Rules, Probabilistic Reasoning, Conditioning, Complexity
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| Leandro Rego | leandro@de.ufpe.br |