Hierarchical Bayesian classification of chirp signals

Abstract

This paper addresses the problem of classifying chirp signals using hierarchical Bayesian learning combined with Markov Chain Monte Carlo (MCMC) methods. Bayesian learning consists of estimating the distribution of observed data conditional upon each class from a set of training samples. Unfortunately, this estimation often requires to evaluate intractable multidimensional integrals. This paper studies an original implementation of hierarchical Bayesian learning which estimates the class conditional probability densities using MCMC methods. The performance of this implementation is compared to other existing approaches for the classification of chirp signals.