Abstract We study the problem of unsupervised learning graphical models in mixed discrete-continuous domains. The such discrete domains alone is notoriously challenging, compounded by fact that inference computationally demanding. situation generally believed to be significantly worse domains: estimating unknown probability distribution given samples often limited practice a handful parametric ...

Statistical inference for discrete-valued time series has not been developed like traditional methods generated by continuous random variables. Some relevant models exist, but the lack of a homogenous framework raises some critical issues. For instance, it is trivial to explore whether are nested and quite arduous derive stochastic properties which simultaneously hold across different specifica...