Entropic priors for hidden-Markov model classification

In pattern classification problems lack of knowledge about the prior distribution is typically filled up with uniform priors. However this choice may lead to unsatisfactory inference results when the amount of observed data is scarce. The application of Maximum Entropy (ME) principle to prior determination results in the so-called en-tropic priors, which provide a much more cautious inference in comparison to uniform priors. The idea, introduced mainly within the context of theoretical physics, is applied here to signal processing scenarios. We derive efficient formulas for computing and updating entropic priors when the the likelihoods follow on Independent, Markov and Hidden Markov models and we apply them to a target-track classification task.