Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rules for standard probabilistic networks to the dynamic case, and show how to search for structure when some of the variables are hidden. Finally, we examine two applications where such a technology might be useful: predicting and classifying dynamic behaviors, and learning causal orderings in biological processes. We provide empirical results that demonstrate the applicability of our methods in both domains. 1

We survey the literature on methods for inference and learning in Bayesian Networks composed of discrete and continuous nodes, in which the continuous nodes have a multivariate Gaussian distribution, whose mean and variance depends on the values of the discrete nodes. We also briefly consider hybrid Dynamic Bayesian Networks, an extension of switching Kalman filters. This report is meant to summarize what is known at a sufficient level of detail to enable someone to implement the algorithms, but without dwelling on formalities.

In this paper, we present two methods to provide explanations for reasoning with belief functions in the valuation-based systems. One approach, inspired by Strat's method, is based on sensitivity analysis, but its computation is simpler thus easier to implement than Strat's. The other one is to examine the impact of evidence on the conclusion based on the measure of the information content in the evidence. We show the property of additivity for the pieces of evidence that are conditional independent within the context of the valuation-based systems. We will give an example to show how these approaches are applied in an evidential network. 1 Introduction The developers of expert systems have realized that a good facility to explain the computer-based reasoning to users is a prerequisite to their more widespread acceptance. The importance of explanation is due to two reasons. First, expert systems are usually used to solve difficult problems. A good explanation facility a...

Probabilistic reasoning in Bayesian networks is normally conducted on a junction tree by repeatedly applying the local propagation whenever new evidence is observed. In this paper, we suggest to treat probabilistic reasoning as database queries. We adapt a method for answering queries in database theory to the setting of probabilistic reasoning in Bayesian networks. We show an e#ective method for probabilistic reasoning without repeated application of local propagation whenever evidence is observed.

We present two methods to provide explanations for reasoning with belief functions. One approach, inspired by Strat's method, is based on sensitivity analysis, but its computation is simpler thus easier to implement than Strat's. The other approach is to examine the impact of each piece of evidence on the conclusion based on the measure of the information content provided by the evidence. We show the property of additivity for each pieces of evidence that are conditional independent within the context of the valuation-based systems. We will give an example to show how these approaches are applied in an evidential network. 1 Introduction The developers of expert systems have realized that a good facility to explain the computer-based reasoning to users is a prerequisite to their more widespread acceptance. The importance of explanation is due to two reasons. First, expert systems are usually used to solve difficult problems. A good explanation facility allows users to observe ...

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