Towards a novel probabilistic graphical model of sequential data: a solution to the problem of structure learning and an empirical evaluation

This paper develops a maximum pseudo-likelihood algorithm for learning the structure of the dynamic extension of Hybrid Random Field introduced in the companion paper [5]. The technique turns out to be a viable method for capturing the statistical (in)dependencies among the random variables within a sequence of patterns. Complexity issues are tackled by means of adequate strategies from classic literature on probabilistic graphical models. A preliminary empirical evaluation is presented eventually.