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A Standard Approach for Optimizing Belief Network Inference using Query DAGs [article]

Adnan Darwiche, Gregory M. Provan
2013 arXiv   pre-print
This paper proposes a novel, algorithm-independent approach to optimizing belief network inference. rather than designing optimizations on an algorithm by algorithm basis, we argue that one should use  ...  We show that our Q-DAG optimizations require time linear in the Q-DAG size, and significantly simplify the process of designing algorithms for optimizing belief network inference.  ...  According to the Q-DAG approach, belief network inference is decomposed into two steps as shown in Figure 1 .  ... 
arXiv:1302.1532v1 fatcat:fbqtgmkzvbb7jeazn5a32f3wni

Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference [article]

A. Darwiche, G. Provan
1997 arXiv   pre-print
We describe a new paradigm for implementing inference in belief networks, which consists of two steps: (1) compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG); and (2) answering  ...  It appears that Q-DAGs can be generated using any of the standard algorithms for exact inference in belief networks (we show how they can be generated using clustering and conditioning algorithms).  ...  Special thanks to Jack Breese, Bruce D'Ambrosio and to the anonymous reviewers for their useful comments on earlier drafts of this paper.  ... 
arXiv:cs/9705101v1 fatcat:xww2cfqcufagvc5pszjjpiptma

Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference

A. Darwiche, G. Provan
1997 The Journal of Artificial Intelligence Research  
We describe a new paradigm for implementing inference in belief networks, which consists of two steps: (1) compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG); and (2) answering  ...  It appears that Q-DAGs can be generated using any of the standard algorithms for exact inference in belief networks (we show how they can be generated using clustering and conditioning algorithms).  ...  Special thanks to Jack Breese, Bruce D'Ambrosio and to the anonymous reviewers for their useful comments on earlier drafts of this paper.  ... 
doi:10.1613/jair.330 fatcat:fjudfaigpvcejatog5d357okg4

Query DAGs: A Practical Paradigm for Implementing Belief Network Inference [article]

Adnan Darwiche, Gregory M. Provan
2014 arXiv   pre-print
We describe a new paradigm for implementing inference in belief networks, which relies on compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG).  ...  It appears that Q-DAGs can be generated using any of the algorithms for exact inference in belief networks --- we show how they can be generated using clustering and conditioning algorithms.  ...  Therefore, if a network can be solved using a standard inference algorithm, we can construct a Q DAG for that network.  ... 
arXiv:1408.1480v1 fatcat:fevsvpdl7zhrdlblgdxl4asrrm

Exploiting Dynamic Independence in a Static Conditioning Graph [chapter]

Kevin Grant, Michael C. Horsch
2006 Lecture Notes in Computer Science  
A conditioning graph (CG) is a graphical structure that attempt to minimize the implementation overhead of computing probabilities in belief networks.  ...  A conditioning graph recursively factorizes the network, but restricting each decomposition to a single node allows us to store the structure with minimal overhead, and compute with a simple algorithm.  ...  The evaluation engine for this approach is very lightweight, reducing system overhead substantially. However, the size of a Q-DAG may be exponential in the size of the network.  ... 
doi:10.1007/11766247_18 fatcat:bwi7r4223nh3dfqtmgfxjirn7m

Any-time probabilistic switching model using bayesian networks

Shiva Shankar Ramani, Sanjukta Bhanja
2004 Proceedings of the 2004 international symposium on Low power electronics and design - ISLPED '04  
A probabilistic Bayesian Network based switching model can explicitly model all spatio-temporal dependency relationships in a combinational circuit, resulting in zero-error estimates.  ...  This paper explores a non-simulative, Importance Sampling based, probabilistic estimation strategy that scales well with circuit complexity.  ...  We first mapped the ISCAS circuits to their corresponding DAG structured Bayesian Networks. The experimental set-up of "GeNIe" [14], a graphical network interface is used for our experimentation.  ... 
doi:10.1145/1013235.1013263 dblp:conf/islped/RamaniB04 fatcat:g2h4kxmdkrfide25oimavpympu

Page 3336 of Mathematical Reviews Vol. , Issue 93f [page]

1993 Mathematical Reviews  
Summary: “Belief networks are important objects for research study and for actual use, as the experience of the MUNIN project demonstrates.  ...  Summary: “Belief networks are tried as a method for propagation of singleton interval probabilities.  ... 

BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks

Han Yu, Janhavi Moharil, Rachael Hageman Blair
2020 Journal of Statistical Software  
A shiny app with Cytoscape widgets provides an interactive interface for evidence absorption, queries, and visualizations.  ...  Probabilistic reasoning enables a user to absorb information into a Bayesian network and make queries about how the probabilities within the network change in light of new information.  ...  The relationships between nodes in a network are described using a standard familial terminology.  ... 
doi:10.18637/jss.v094.i03 fatcat:2pgpbq3ijnfbpl762sm3pzxzd4

Chapter 11 Bayesian Networks [chapter]

A. Darwiche
2008 Foundations of Artificial Intelligence  
A Bayesian network is a tool for modeling and reasoning with uncertain beliefs.  ...  These causal Bayesian networks lead to additional types of queries, and require more specialized algorithms for computing them. Causal Bayesian networks will be discussed in Section 11.6. 468 11.  ...  Inference Inference on Bayesian networks generated from high-level specifications can be performed using standard inference algorithms discussed earlier.  ... 
doi:10.1016/s1574-6526(07)03011-8 fatcat:yypbezypcbeazo6ehprmqrrdci

Real-Time Inference with Large-Scale Temporal Bayes Nets [article]

Masami Takikawa, Bruce D'Ambrosio, Ed Wright
2012 arXiv   pre-print
We have developed a new computational approach to support real-time exact inference in large temporal Bayes nets.  ...  We approach the real-time issue by organizing temporal Bayes nets into static representations, and then using the symbolic probabilistic inference algorithm to derive analytic expressions for the static  ...  Funding for this research is provided in part under the MDA (formerly, BMDO) Hercules Program contract# HQ0006-02-C-0004.  ... 
arXiv:1301.0603v1 fatcat:qnwco3wfcbh2vppsoibbuaj7pu

Conditioning Graphs: Practical Structures for Inference in Bayesian Networks [chapter]

Kevin Grant, Michael C. Horsch
2005 Lecture Notes in Computer Science  
The standard approach to probabilistic inference in Bayesian networks is to compile the graph into a join-tree, and perform computation over this secondary structure.  ...  Probability is a useful tool for reasoning when faced with uncertainty.  ...  Algorithms exist to optimize structure [28] , handle evidence dynamically [13, 27] , and run query-driven inference (for generating beliefs over small subsets of variables; this requires that functions  ... 
doi:10.1007/11589990_8 fatcat:5kpd6og4czcnxkpkbpipd3fqjq

A multi-agent systems approach to distributed bayesian information fusion

Gregor Pavlin, Patrick de Oude, Marinus Maris, Jan Nunnink, Thomas Hood
2010 Information Fusion  
The modularization and combination principles are used in Distributed Perception Networks (DPN), a multi-agent fusion architecture.  ...  With the help of the theory of Bayesian networks and factor graphs we derive design and organization rules for modular fusion systems which implement exact belief propagation without centralized configuration  ...  Concise queries can be answered by a simple yes or no, which is used as evidence about different symptoms in DPN agents that generated the queries.  ... 
doi:10.1016/j.inffus.2009.09.007 fatcat:xl4hd6k5bbhenl6ro46kr6hkju

Combining link and content-based information in a Bayesian inference model for entity search

Christos L. Koumenides, Nigel R. Shadbolt
2012 Proceedings of the 1st Joint International Workshop on Entity-Oriented and Semantic Search - JIWES '12  
An architectural model of a Bayesian inference network to support entity search in semantic knowledge bases is presented.  ...  A flexible query model is supported capable to reason with the availability of simple semantics in queries.  ...  BAYESIAN INFERENCE NETWORKS Bayesian belief networks [11] are among the best understood stochastic methods for modelling joint probability distributions within a domain of interest.  ... 
doi:10.1145/2379307.2379310 fatcat:yaqzv5us5fac3cgmmejoxnh2ui

Belief Propagation for Structured Decision Making [article]

Qiang Liu, Alexander T. Ihler
2012 arXiv   pre-print
In this work, we present a general variational framework for solving structured cooperative decision-making problems, use it to propose several belief propagation-like algorithms, and analyze them both  ...  Variational inference algorithms such as belief propagation have had tremendous impact on our ability to learn and use graphical models, and give many insights for developing or understanding exact and  ...  Work supported in part by NSF IIS-1065618 and a Microsoft Research Fellowship.  ... 
arXiv:1210.4897v1 fatcat:tpb6bigdxjh2bm5q4472or65mu

Learning Bayesian networks: approaches and issues

Rónán Daly, Qiang Shen, Stuart Aitken
2011 Knowledge engineering review (Print)  
Bayesian networks have become a widely used method in the modelling of uncertain knowledge.  ...  This article is not intended to be a tutorial-for this, there are many books on the topic, which will be presented.  ...  Stuart Aitken is funded by BBSRC grant BB/F015976/1, and by the Centre for Systems Biology at Edinburgh, a Centre for Integrative Systems Biology (CISB) funded by BBSRC and EPSRC, reference BB/ D019621  ... 
doi:10.1017/s0269888910000251 fatcat:schmhrymdjewrggsdq7vmx23uu
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