Dynamic Probabilistic Networks for Modelling and Identifying Dynamic Systems: a MCMC Approach

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.

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