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We present algorithms for the generation of uniformly distributed Bayesian networks with constraints on induced width. The algorithms use ergodic Markov chains to generate samples. The introduction of constraints on induced width leads to realistic networks but requires new techniques. A tool that generates random networks is presented and applications are… (More)

This paper presents new methods for generation of random Bayesian networks. Such methods can be used to test inference and learning algorithms for Bayesian networks, and to obtain insights on average properties of such networks. Any method that generates Bayesian networks must first generate directed acyclic graphs (the " structure " of the network) and… (More)

This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise , indeterminate and qualitative probabilis-tic assessments. The paper shows how this can be achieved… (More)

Credal networks generalize Bayesian networks relaxing numerical parameters. This considerably expands expressivity, but makes belief updating a hard task even on polytrees. Nevertheless, if all the variables are binary, polytree-shaped credal networks can be efficiently updated by the 2U algorithm. In this paper we present a binarization algorithm, that… (More)

Graph-theoretical representations for sets of probability measures (credal networks) generally display high complexity, and approximate inference seems to be a natural solution for large networks. This paper introduces a variational approach to approximate inference in credal networks: we show how to formulate mean field approximations using naive (fully… (More)

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