We propose LAZY arc-reversal with variable elimination (LAZY-ARVE) as a new approach to probabilistic inference in Bayesian networks (BNs). LAZY-ARVE is an improvement upon LAZY arcreversal (LAZY-AR), which was very recently proposed and empirically shown to be the state-of-the-art method for exact inference in discrete BNs. The primary advantage of LAZY-ARVE over LAZY-AR is that the former onl...

Even though existing algorithms for belief update in Bayesian networks (BNs) have exponential time and space complexity, belief update in many real-world BNs is feasible. However, in some cases the efficiency of belief update may be insufficient. In such cases minor improvements in efficiency may be important or even necessary to make a task tractable. This paper introduces two improvements to ...

In this paper we show how Bayesian networks (BNs) can be used for modeling other agents in the environment. BNs are a compact representation of a joint probability distribution. More precisely we will have special attention to the problem of large state spaces and incomplete information. To test our techniques experimentally, we will consider the robotic soccer simulation. Robotic soccer client...

Bayesian networks (BNs) constitute a useful tool to model the joint distribution of a set of random variables of interest. This paper is concerned with the network induction problem. We propose a number of hybrid recombination operators for extracting BNs from data. These hybrid operators make use of phenotypic information in order to guide the processing of information during recombination. Th...

In this paper, we suggest the model for the context aware computing of the home network systems. Our model uses the Bayesian networks (BNs) and a new approach to structure learning of BNs based on genetic algorithm is proposed. The proposed method explores the wider solution space than the previous method which also uses the genetic algorithm for structure learning of BNs. In the previous metho...

This paper introduces a new probabilistic graphical model called gated Bayesian network (GBN). This model evolved from the need to represent real world processes that include several distinct phases. In essence a GBN is a model that combines several Bayesian networks (BN) in such a manner that they may be active or inactive during queries to the model. We use objects called gates to combine BNs...

Bayesian Networks (BNs) are useful tools giving a natural and compact representation of joint probability distributions. In many applications one needs to learn a Bayesian Network (BN) from data. In this context, it is important to understand the number of samples needed in order to guarantee a successful learning. Previous works have studied BNs sample complexity, yet they mainly focused on th...

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...

Modelling cause and effect relationships has been a major challenge for statisticians in a wide range of application areas. Bayesian Networks (BN) combine graphical analysis with Bayesian analysis to represent causality maps linking measured and target variables. Such maps can be used for diagnostics and predictive analytics. The paper presents an introduction to Bayesian Networks and various a...

The majority of Procedural Content Generation (PCG) research has made use of human authored rules, heuristics and evaluation metrics. Machine learning techniques have gone relatively unused in PCG. We introduce a data-driven level generation approach, and apply it to the of dungeons for Zelda-like Action Roleplaying Games (ARPGs). We use Bayesian Networks (BNs) to learn distributional informati...