scholarly journals AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks

2000 ◽  
Vol 13 ◽  
pp. 155-188 ◽  
Author(s):  
J. Cheng ◽  
M. J. Druzdzel
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Importance Sampling ◽  
Network Models ◽  
Evidential Reasoning ◽  
The Other ◽  
Sampling Algorithm ◽  
Importance Function ◽  
Sampling Algorithms ◽  
Bayesian Network Models

Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm. We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung & Chang, 1989; Shachter & Peot, 1989) and self-importance sampling (Shachter & Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PathFinder network (Heckerman, Horvitz, & Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, & Druzdzel, 1997), with evidence as unlikely as 10^-41. While the AIS-BN algorithm always performed better than the other two algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN algorithm.

Bayesian Network: Probabilistic Reasoning, Statistical Learning, and Applications

2004 ◽  
Vol 8 (2) ◽  
pp. 93-99 ◽  
Author(s):  
Yoichi Motomura ◽  
Keyword(s):  
Information Processing ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Statistical Learning ◽  
Probabilistic Models ◽  
Probabilistic Reasoning ◽  
Network Models ◽  
Intelligent Information Processing ◽  
Intelligent Information ◽  
Bayesian Network Models

Bayesian networks are probabilistic models that can be used for prediction and decision-making in the presence of uncertainty. For intelligent information processing, probabilistic reasoning based on Bayesian networks can be used to cope with uncertainty in real-world domains. In order to apply this, we need appropriate models and statistical learning methods to obtain models. We start by reviewing Bayesian network models, probabilistic reasoning, statistical learning, and related researches. Then, we introduce applications for intelligent information processing using Bayesian networks.

scholarly journals Classifying antimicrobial and multifunctional peptides with Bayesian network models

2018 ◽  
Vol 110 (4) ◽  
pp. e24079 ◽  
Author(s):  
Rainier Barrett ◽  
Shaoyi Jiang ◽  
Andrew D. White
Keyword(s):  
Bayesian Network ◽  
Network Models ◽  
Multifunctional Peptides ◽  
Bayesian Network Models

Bayesian Network Modeling of Transcription Factor Binding Sites

2007 ◽  
pp. 300-318
Author(s):  
Vipin Narang ◽  
Rajesh Chowdhary ◽  
Ankush Mittal ◽  
Wing-Kin Sung
Keyword(s):  
Transcription Factor ◽  
Parameter Estimation ◽  
Bayesian Network ◽  
Network Modeling ◽  
Network Models ◽  
Transcription Factor Binding Sites ◽  
Physical Problem ◽  
Network Applications ◽  
Bayesian Network Modeling ◽  
Bayesian Network Models

A predicament that engineers who wish to employ Bayesian networks to solve practical problems often face is the depth of study required in order to obtain a workable understanding of this tool. This chapter is intended as a tutorial material to assist the reader in efficiently understanding the fundamental concepts involved in Bayesian network applications. It presents a complete step by step solution of a bioinformatics problem using Bayesian network models, with detailed illustration of modeling, parameter estimation, and inference mechanisms. Considerations in determining an appropriate Bayesian network model representation of a physical problem are also discussed.

Retrieving Medical Records Using Bayesian Networks

2011 ◽  
pp. 2274-2280
Author(s):  
Luis M. De Campos
Keyword(s):  
Information Retrieval ◽  
Bayesian Networks ◽  
Graphical Models ◽  
Medical Records ◽  
Network Models ◽  
Boolean Expression ◽  
Wide Range ◽  
Structured Information Retrieval ◽  
Structured Information ◽  
Bayesian Network Models

Bayesian networks (Jensen, 2001) are powerful tools for dealing with uncertainty. They have been successfully applied in a wide range of domains where this property is an important feature, as in the case of information retrieval (IR) (Turtle & Croft, 1991). This field (Baeza-Yates & Ribeiro- Neto, 1999) is concerned with the representation, storage, organization, and accessing of information items (the textual representation of any kind of object). Uncertainty is also present in this field, and, consequently, several approaches based on these probabilistic graphical models have been designed in an attempt to represent documents and their contents (expressed by means of indexed terms), and the relationships between them, so as to retrieve as many relevant documents as possible, given a query submitted by a user. Classic IR has evolved from flat documents (i.e., texts that do not have any kind of structure relating their contents) with all the indexing terms directly assigned to the document itself toward structured information retrieval (SIR) (Chiaramella, 2001), where the structure or the hierarchy of contents of a document is taken into account. For instance, a book can be divided into chapters, each chapter into sections, each section into paragraphs, and so on. Terms could be assigned to any of the parts where they occur. New standards, such as SGML or XML, have been developed to represent this type of document. Bayesian network models also have been extended to deal with this new kind of document. In this article, a structured information retrieval application in the domain of a pathological anatomy service is presented. All the medical records that this service stores are represented in XML, and our contribution involves retrieving records that are relevant for a given query that could be formulated by a Boolean expression on some fields, as well as using a text-free query on other different fields. The search engine that answers this second type of query is based on Bayesian networks.

scholarly journals Bayesian Network Models in Cyber Security: A Systematic Review

2017 ◽  
pp. 105-122 ◽  
Author(s):  
Sabarathinam Chockalingam ◽  
Wolter Pieters ◽  
André Teixeira ◽  
Pieter van Gelder
Keyword(s):  
Systematic Review ◽  
Bayesian Network ◽  
Cyber Security ◽  
Network Models ◽  
Bayesian Network Models
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