Dynamic probabilistic networks for modelling and identifying dynamic systems: A MCMC approach

Riccardo Bellazzi, Paolo Magni, Giuseppe De Nicolao

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)245-262
Number of pages18
JournalIntelligent Data Analysis
Volume1
Issue number4
DOIs
Publication statusPublished - 1997

Keywords

  • Bayesian smoothing
  • Causal probabilistic networks
  • Dynamic systems
  • Markov chain Monte Carlo methods

ASJC Scopus subject areas

  • Artificial Intelligence
  • Theoretical Computer Science
  • Computer Vision and Pattern Recognition

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