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 ...

This paper discusses the application of belief propagation to the reconstruction of bathymetric sonar images. Belief propagation is a powerful technique for probabilistic reasoning on Bayesian and Markov networks. It allows a priori information to be easily incorporated, in this case the expected seafloor height variation. We show that the algorithm works well without multipath interference but...

Special-case algorithms for Bayesian belief networks are designed to alleviate the computational burden of problem solving. These algorithms provide a case base for storing solutions for a small number of situations that are likely to be encountered during problem solving. This case base is employed as a lter for belief-network inference: for a problem under consideration, the network at hand i...

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...

We provide an overview of recent research on belief and opinion dynamics in social networks. We discuss both Bayesian and non-Bayesian models of social learning and focus on the implications of the form of learning (e.g., Bayesian vs. non-Bayesian), the sources of information (e.g., observation vs. communication), and the structure of social networks in which individuals are situated on three k...

Probabilistic inference in Bayesian networks, and even reasoning within error bounds are known to be NP-hard problems. Our research focuses on investigating approximate message-passing algorithms inspired by Pearl’s belief propagation algorithm and by variable elimination. We study the advantages of bounded inference provided by anytime schemes such as Mini-Clustering (MC), and combine them wit...

The purpose of this paper is to identify the medical risk applying big data technology and build a (MBDR) control process manage from systematic perspective. In process, we firstly used literature reviews (SLRs) method systematically search 322 papers in web science with topics “medical risk” “big dimensional system theoretical level. Based on case study hospital Shanghai, explored formation me...

In this paper I give a brief overview of recent work on uncertainty inAI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks for reasoning that emphasize different aspects of intelligent reasoning. Belief networks (Bayesian networks) are representations of independence that form the basis for understanding much of the recent work on reason...