A Bayesian provedure to recognize independent signals

We propose a Bayesian test to assess the statistical dependence when only a small number of samples are available. Our procedure converts the problem of independence test to a parametric one through quantization and computes the likelihood of the observed cell counts under the independence hypothesis where the marginal cell probabilities are modeled by independent symmetric Dirichlet priors. We tested the ability of our Bayesian test in validating the solutions to the problem of blind source separation. The experimental results indicate that while the standard parametric method frequently fails to distinguish the case of independent signals from dependent ones due to the deviation of the test statistic from its desired distribution, our approach can overcome the scarcity of data samples with a proper selection of the prior parameters to achieve a significantly superior performance