Hidden markov models (HMMs) have the interesting property that they can be used to model mixtures of populations for dependent data without prior parametric assumptions on the populations. HMMs can be used to build flexible priors.
I will present recent results on empirical Bayes multiple testing, non parametric inference of HMMs and fundamental limits in the learning of HMMs.

Hidden markov models (HMMs) have the interesting property that they can be used to model mixtures of populations for dependent data without prior parametric assumptions on the populations. HMMs can be used to build flexible priors.
I will present recent results on empirical Bayes multiple testing, non parametric inference of HMMs and fundamental limits in the learning of HMMs.