Dynamic Probabilistic Networks for Modelling and Identifying Dynamic Systems: A MCMC Approach^☆

Article type: Research Article

Authors: Bellazzi, Riccardo | Magni, Paolo | De Nicolao, Giuseppe

Affiliations: Dipartimento di Informatica e Sistemistica, Università di Pavia, via Ferrata 1, I-27100 Pavia, Italy

Note: [☆] Part of this paper has been presented at the ECAI'96 conference in Budapest.

Abstract: In this article we deal with the problem of interpreting data coming from a dynamic system by using causal probabilistic (CPN), a probabilistic graphical model particularly appealing in Intelligent Data Analysis. We discuss the different approaches presented in the literature, outlining their pros and cons through a simple training example. Then, we present a new method for reconstructing the state of the dynamic system, based on Markov Chain Monte Carlo algorithms, called dynamic probabilistic network smoothing (DPN-smoothing). Finally, we present an example of the application of DPN-smoothing in the field of signal deconvolution.

Keywords: Causal probabilistic networks, Dynamic systems, Markov chain Monte Carlo methods, Bayesian smoothing

DOI: 10.3233/IDA-1997-1403

Journal: Intelligent Data Analysis, vol. 1, no. 4, pp. 245-262, 1997