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 .
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.
Book Title: Lecture Notes in Computer Science