Conditioning in Chaotic Probabilities Interpreted as a Generalized
Leandro Chaves R, Recife-PE Brazil · 2007
We propose a new deflnition 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 M endowed with an 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 flnite 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.