Convergence Results for Relational Bayesian
نویسنده
چکیده
Relational Bayesian networks are an extension of the method of probabilistic model construction by Bayes-ian networks. They deene probability distributions on nite relational structures by conditioning the probability of a ground atom r(a 1 ; : : : ; a n) on rst-order properties of a 1 ; : : : ; a n that have been established by previous random decisions. In this paper we investigate from a nite model theory perspective the convergence properties of the distributions deened in this manner. A subclass of relational Bayesian networks is identiied that deene distributions with convergence laws for rst-order properties.
منابع مشابه
Structure Learning in Bayesian Networks Using Asexual Reproduction Optimization
A new structure learning approach for Bayesian networks (BNs) based on asexual reproduction optimization (ARO) is proposed in this letter. ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survi...
متن کاملGenetic Properties of Some Economic Traits in Isfahan Native Fowl Using Bayesian and REML Methods
The objective of the present study was to estimate heritability values for some performance and egg quality traits of native fowl in Isfahan breeding center using REML and Bayesian approaches. The records were about 51521 and 975 for performance and egg quality traits, respectively. At the first step, variance components were estimated for body weight at hatch (BW0), body weight at 8 weeks of a...
متن کاملImportance Sampling on Relational Bayesian Networks
We present techniques for importance sampling from distributions defined by Relational Bayesian Networks. The methods operate directly on the abstract representation language, and therefore can be applied in situations where sampling from a standard Bayesian Network representation is infeasible. We describe experimental results from using standard, adaptive and backward sampling strategies. Fur...
متن کاملSimple Estimators for Relational Bayesian Classifiers
In this paper we present the Relational Bayesian Classifier (RBC), a modification of the Simple Bayesian Classifier (SBC) for relational data. There exist several Bayesian classifiers that learn predictive models of relational data, but each uses a different estimation technique for modeling heterogeneous sets of attribute values. The effects of data characteristics on estimation have not been ...
متن کاملCompiling relational Bayesian networks for exact inference
We describe in this paper a system for exact inference with relational Bayesian networks as defined in the publicly available Primula tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by evaluating and differentiating these circuits in time linear in their size. We report on experimental r...
متن کامل