This paper presents a new deterministic approximation technique in Bayesian networks. This method, “Expectation Propagation,” unifies two previous techniques: assumed-density filtering, an extension of the Kalman filter, and loopy belief propagation, an extension of belief propagation in Bayesian networks. Loopy belief propagation, because it propagates exact belief states, is useful for a limi...

The application of probabilistic reasoning to fault diagnosis in linear lightwave networks (LLNs) is investigated. The LLN inference model is represented by a Bayesian network (or causal network). An inference algorithm is proposed that is capable of conducting fault diagnosis (inference) with incomplete evidence and on an interactive basis. Two belief updating algorithms are presented which ar...

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...

This paper presents a Bayesian belief network (BBN) approach for socio technical system reliability assessment. A human error model (BBN) quantifies error influences arising from user knowledge, ability and task environment, combined with factors describing the complexity of user action and user interface quality. System reliability evaluation is achieved by the Scenario Reliability analyser to...

MASTS Annual Science Meeting Title: Additional insight on potential reallocation scenarios for artisanal fisheries from a spatialBayesian belief network

A simple model of rational belief holds that: (i) an instantaneous snapshot of an ideally rational belief system corresponds to a probability distribution; and (ii) rational belief change occurs by Bayesian conditionalization. But a priori probability distributions of the Kolmogorov sort cannot distinguish between propositions that are simply true from propositions that are necessarily true. Fu...

With the large number of clinical practice guidelines available, there is an increasing need for a comprehensive unified model for acute ischemic stroke treatment to assist in clinical decision making. We present a unified treatment model derived through review of existing clinical practice guidelines, meta-analyses, and clinical trials. Using logic from the treatment model, a Bayesian belief n...