A factor graph inference algorithm for diagnostic Bayesian networks

AbstractBayesian networks in their Factor Graph Reduced Normal Form are a powerful paradigm for implementing inference graphs. Unfortunately, the computational and memory costs of these networks may be considerable even for relatively small networks, and this is one of the main reasons why these structures have often been underused in practice. In this work, through a detailed algorithmic and structural analysis, various solutions for cost reduction are proposed. Moreover, an online version of the classic batch learning algorithm is also analysed, showing very similar results in an unsupervised context but with much better performance; which may be essential if multi-level structures are to be built. The solutions proposed, together with the possible online learning algorithm, are included in a C++ library that is quite efficient, especially if compared to the direct use of the well-known sum-product and Maximum Likelihood algorithms. The results obtained are discussed with particular reference to a Latent Variable Model structure.

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A factor tree inference algorithm for Bayesian networks and its applications

16th IEEE International Conference on Tools with Artificial Intelligence TAI-04 ◽

10.1109/ictai.2004.9 ◽

2005 ◽

Cited By ~ 2

Author(s):

Wenhui Liao ◽

Weihong Zhang ◽

Qiang Ji

Keyword(s):

Bayesian Networks ◽

Inference Algorithm ◽

Tree Inference

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Complex System Testability Analysis Based on Bayesian Networks under Small Sample

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.1772 ◽

2014 ◽

Vol 602-605 ◽

pp. 1772-1777

Author(s):

Xi Shan Zhang ◽

Kao Li Huang ◽

Peng Cheng Yan ◽

Guang Yao Lian

Keyword(s):

Complex System ◽

Quantitative Analysis ◽

Bayesian Networks ◽

Bayesian Network ◽

Prior Information ◽

Learning Algorithm ◽

Small Sample ◽

Inference Algorithm ◽

Network Learning ◽

Testability Analysis

A lot of prior information in complex system test has been accumulated. To use the prior information for complex system testability quantitative analysis, a new complex system testability modeling and analyze method based on Bayesian network is presented. First, the complex system’s testability model is built using various kind of prior information by Bayesian network learning algorithm. Then, the way of assessing the testability of complex system is provided using the inference algorithm of Bayesian network. Finally, some proper examples are provided to prove the method’s validity.

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Structured Bayesian Networks: From Inference to Learning with Routes

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33017957 ◽

2019 ◽

Vol 33 ◽

pp. 7957-7965

Author(s):

Yujia Shen ◽

Anchal Goyanka ◽

Adnan Darwiche ◽

Arthur Choi

Keyword(s):

Bayesian Networks ◽

Graphical Models ◽

Learning Algorithm ◽

Simple Algorithm ◽

Gps Data ◽

Inference Algorithm ◽

Exact Inference ◽

Polynomial Size ◽

Traditional Approaches ◽

Domain Constraints

Structured Bayesian networks (SBNs) are a recently proposed class of probabilistic graphical models which integrate background knowledge in two forms: conditional independence constraints and Boolean domain constraints. In this paper, we propose the first exact inference algorithm for SBNs, based on compiling a given SBN to a Probabilistic Sentential Decision Diagram (PSDD). We further identify a tractable subclass of SBNs, which have PSDDs of polynomial size. These SBNs yield a tractable model of route distributions, whose structure can be learned from GPS data, using a simple algorithm that we propose. Empirically, we demonstrate the utility of our inference algorithm, showing that it can be an order-ofmagnitude more efficient than more traditional approaches to exact inference. We demonstrate the utility of our learning algorithm, showing that it can learn more accurate models and classifiers from GPS data.

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