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Hybrid algorithms for approximate belief updating in Bayes nets

Eugene Santos, Solomon Eyal Shimony, Edward Williams
<span title="">1997</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sy2zvsxl4vdejh3zsp3utmplry" style="color: black;">International Journal of Approximate Reasoning</a> </i> &nbsp;
Belief updating in Bayes nets, a well-known computationally hard problem, has recently been approximated by several deterministic algorithms and by various randomized approximation algorithms.  ...  Genetic algorithms can be used as an alternate search component for high-probability instantiations; several methods of applying them to belief updating are presented. © 1997 Elsevier Science Inc.  ...  ACKNOWLEDGMENTS This research was supported in part by AFOSR Project 940006, and by the Paul Ivanier Center for Robotics and Production Management, Ben-Gurion University.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0888-613x(97)00012-1">doi:10.1016/s0888-613x(97)00012-1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xofw3mvxbvgejmo4eu3g7ctk3y">fatcat:xofw3mvxbvgejmo4eu3g7ctk3y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170928063536/http://publisher-connector.core.ac.uk/resourcesync/data/elsevier/pdf/60a/aHR0cDovL2FwaS5lbHNldmllci5jb20vY29udGVudC9hcnRpY2xlL3BpaS9zMDg4ODYxM3g5NzAwMDEyMQ%3D%3D.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a6/c3/a6c360408abd4a69b6c9decbe2223844e32e3ccb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0888-613x(97)00012-1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Page 2577 of Mathematical Reviews Vol. , Issue 98D [page]

<span title="">1998</span> <i title="American Mathematical Society"> <a target="_blank" rel="noopener" href="https://archive.org/details/pub_mathematical-reviews" style="color: black;">Mathematical Reviews </a> </i> &nbsp;
); Williams, Edward (1-AFIT-EL; Wright-Patterson AFB, OH) Hybrid algorithms for approximate belief updating in Bayes nets.  ...  Summary: “Belief updating in Bayes nets, a well-known computa- tionally hard problem, has recently been approximated by several deterministic algorithms and by various randomized approxima- tion algorithms  ... 
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Special issue on the 1996 uncertainty in AI (UAI'96) conference—preface

Piero P. Bonissone
<span title="">1997</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sy2zvsxl4vdejh3zsp3utmplry" style="color: black;">International Journal of Approximate Reasoning</a> </i> &nbsp;
Every year, the Uncertainty in Artificial Intelligence (UAI) Conference provides a highly selected forum in which the most important innovations in the field of reasoning with uncertain, imprecise, and  ...  As the editor-in-chief of IJAR, I encouraged some of the authors of papers presented at UAI'96 to extend their original material, incorporating the feedback received during the conference, and presenting  ...  In the third paper, "Hybrid Algorithms for Approximate Belief Updating in Bayes Nets," Santos, Shimony, and Williams address the issue of computing marginal probabilities in a multiply connected Bayes  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0888-613x(97)00022-4">doi:10.1016/s0888-613x(97)00022-4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lq2iblwdsbdshcb3btkrsr7mau">fatcat:lq2iblwdsbdshcb3btkrsr7mau</a> </span>
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Hybrid Variational/Gibbs Collapsed Inference in Topic Models [article]

Max Welling, Yee Whye Teh, Hilbert Kappen
<span title="2012-06-13">2012</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a hybrid algorithm that combines the best of both worlds: it samples very small counts and applies variational updates to large counts.  ...  Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks.  ...  All of these factors are local in the cliques of the original Bayes net.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1206.3297v1">arXiv:1206.3297v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zwl4xo2fifhmpksua566bzlksa">fatcat:zwl4xo2fifhmpksua566bzlksa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191025192641/https://arxiv.org/pdf/1206.3297v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ab/e1/abe1fc2084da09935371287d7816fad40fa19526.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1206.3297v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Expectation Propagation for approximate Bayesian inference [article]

Thomas P. Minka
<span title="2013-01-10">2013</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Expectation Propagation also provides an efficient algorithm for training Bayes point machine classifiers.  ...  All three algorithms try to recover an approximate distribution which is close in KL divergence to the true distribution.  ...  SUMMARY This paper presented a generalization of belief propagation which is appropriate for hybrid belief networks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1301.2294v1">arXiv:1301.2294v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yh6dx75vk5etnm47x2hvwnus3m">fatcat:yh6dx75vk5etnm47x2hvwnus3m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200930194323/https://arxiv.org/ftp/arxiv/papers/1301/1301.2294.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/eb/b3/ebb3725deb8bd1f8c595ddb629a201971fb83fe1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1301.2294v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Learning, prediction and causal Bayes nets

Clark Glymour
<span title="">2003</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2gvktplqc5gm7mwsea6qgdwy4m" style="color: black;">Trends in Cognitive Sciences</a> </i> &nbsp;
Recent research in cognitive and developmental psychology on acquiring and using causal knowledge uses the causal Bayes net formalism, which simultaneously represents hypotheses about causal relations,  ...  With the exception of studies of perceptual clues for causal judgments, [5 -7], late 20th century cognitive psychology chiefly added some computational mechanisms: hybrid computational models for the logical  ...  Bayesian Bayes nets learners are naturally suited for 'one-step' updating without memory of past data [37] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s1364-6613(02)00009-8">doi:10.1016/s1364-6613(02)00009-8</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/12517358">pmid:12517358</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gntvx7mgqvdjdonyrjlor6jce4">fatcat:gntvx7mgqvdjdonyrjlor6jce4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170813234150/http://www.ucl.ac.uk/~ucgajpd/Academic/Evidence/downloads/Causality/Psychology/glymour2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6a/00/6a003127ee90945cbc6e8b643bb2b7e01daea37c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s1364-6613(02)00009-8"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers

Russell Greiner, Xiaoyuan Su, Bin Shen, Wei Zhou
<span title="">2005</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/h4nnd7sxwzcwhetu5qkjbcdh6u" style="color: black;">Machine Learning</a> </i> &nbsp;
Bayesian belief nets (BNs) are often used for classification tasks -typically to return the most likely class label for each specified instance.  ...  This paper therefore presents empirical evidence that ELR produces effective classifiers, often superior to the ones produced by the standard "generative" algorithms, especially in common situations where  ...  Schuurmans, and the anonymous reviewers for their many helpful suggestions. We also thank J. Cheng and T. Joachims for letting us use their POWERCON-STRUCTOR and SVM-Light systems.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10994-005-0469-0">doi:10.1007/s10994-005-0469-0</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uk3hwjcju5hkbatedbsmcfjfka">fatcat:uk3hwjcju5hkbatedbsmcfjfka</a> </span>
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Bayesian Inference Using Gibbs Sampling in Applications and Curricula of Decision Analysis

Mauricio Diaz, Daniel M. Frances
<span title="">2014</span> <i title="Institute for Operations Research and the Management Sciences (INFORMS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7vrucex74zbrxaarrhx2epudwu" style="color: black;">INFORMS Transactions on Education</a> </i> &nbsp;
A pplications and curricula of decision analysis currently do not include methods to compute Bayes' rule and obtain posteriors for nonconjugate prior distributions.  ...  BUGS is useful in making optimal decisions, and it is easy to learn and implement; therefore, including BUGS in decision analysis curricula is valuable.  ...  Now the prior distribution must be updated using the three new data points for recruitment in order to solve for the optimal fishing capacity for years 3 through 12.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1287/ited.2013.0120">doi:10.1287/ited.2013.0120</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bkjdtlvrkrbxtgvdzj3wov3rmm">fatcat:bkjdtlvrkrbxtgvdzj3wov3rmm</a> </span>
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Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes [article]

Manuel Haussmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir
<span title="2021-02-26">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper presents a recipe to improve the prediction accuracy of such models in three steps: i) accounting for epistemic uncertainty by assuming probabilistic weights, ii) incorporation of partial knowledge  ...  Neural Stochastic Differential Equations model a dynamical environment with neural nets assigned to their drift and diffusion terms.  ...  Same training objective as (i), however with the hybrid model as proposed in (6) . (iv) E-PAC-Bayes-Hybrid.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.09914v3">arXiv:2006.09914v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vobn5es4gjeypeehyevqq3zevy">fatcat:vobn5es4gjeypeehyevqq3zevy</a> </span>
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A Review of Inference Algorithms for Hybrid Bayesian Networks

Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
<span title="2018-08-30">2018</span> <i title="AI Access Foundation"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4ax4efcwajcgvidb6hcg6mwx4a" style="color: black;">The Journal of Artificial Intelligence Research</a> </i> &nbsp;
In this paper we provide an overview of the main trends and principled approaches for performing inference in hybrid Bayesian networks.  ...  However, this extra feature also comes at a cost: inference in these types of models is computationally more challenging and the underlying models and updating procedures may not even support closed-form  ...  AMIDST has received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no 619209.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1613/jair.1.11228">doi:10.1613/jair.1.11228</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vhmuf44ftbg73mjygiyrwrdw44">fatcat:vhmuf44ftbg73mjygiyrwrdw44</a> </span>
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Target identification with Bayesian networks

Sampsa K. Hautaniemi, Petri T. Korpisaari, Jukka P. P. Saarinen, Belur V. Dasarathy
<span title="2000-04-03">2000</span> <i title="SPIE"> Sensor Fusion: Architectures, Algorithms, and Applications IV </i> &nbsp;
Sekä diskreetin että jatkuvan Bayes-verkon teoria tunnetaan ennestään melko hyvin, vaikka Bayes-verkkojen teoria on erittäin nuori.  ...  Bayes-verkko on ajasta riippumaton, mutta koska tyyppitunnistus on luonteeltaan dynaamista, on työssä esitelty menetelmä, jolla Bayes-verkko saadaan dynaamiseksi.  ...  order to update variable's belief.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.381665">doi:10.1117/12.381665</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pul5sosbb5b6rnnrethf6pis6q">fatcat:pul5sosbb5b6rnnrethf6pis6q</a> </span>
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Sample-and-Accumulate Algorithms for Belief Updating in Bayes Networks [article]

Eugene Santos Jr., Solomon Eyal Shimony, Edward Williams
<span title="2013-02-13">2013</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Belief updating in Bayes nets, a well known computationally hard problem, has recently been approximated by several deterministic algorithms, and by various randomized approximation algorithms.  ...  We present randomized algorithms that enumerate high-probability partial instantiations, resulting in probability bounds. Some of these algorithms are also sampling algorithms.  ...  Acknowledgements This research was supported in part by AFOSR Project #940006, and by the Paul Ivanier center for robotics, Ben-Gurion University.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1302.3602v1">arXiv:1302.3602v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gz6ccvxqq5ah7dy6gbqulqfcwi">fatcat:gz6ccvxqq5ah7dy6gbqulqfcwi</a> </span>
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Belief Propagation for Structured Decision Making [article]

Qiang Liu, Alexander T. Ihler
<span title="2012-10-16">2012</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1210.4897v1">arXiv:1210.4897v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tpb6bigdxjh2bm5q4472or65mu">fatcat:tpb6bigdxjh2bm5q4472or65mu</a> </span>
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Active Machine Learning for Consideration Heuristics

Daria Dzyabura, John R. Hauser
<span title="">2011</span> <i title="Institute for Operations Research and the Management Sciences (INFORMS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3m35i4il6fg6ldbdzunk3e2tue" style="color: black;">Marketing science (Providence, R.I.)</a> </i> &nbsp;
To update posteriors after each question, we approximate the posterior with a variational distribution and use belief propagation (iterative loops of Bayes updating).  ...  Eric Bradlow and then Preyas Desai served as the editor-in-chief and Fred Feinberg served as associate editor for this article.  ...  Acknowledgments This research was supported by the MIT Sloan School of Management, the Center for Digital Business at MIT (http://ebusiness.mit.edu), and an unnamed American automotive manufacturer.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1287/mksc.1110.0660">doi:10.1287/mksc.1110.0660</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iy3g2p57qjaifbsgzfax2sl3zq">fatcat:iy3g2p57qjaifbsgzfax2sl3zq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190225085614/http://pdfs.semanticscholar.org/6023/4664fe4b3148665afff654c093338b5c05e4.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/60/23/60234664fe4b3148665afff654c093338b5c05e4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1287/mksc.1110.0660"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A General Algorithm for Approximate Inference and its Application to Hybrid Bayes Nets [article]

Daphne Koller, Uri Lerner, Dragomir Anguelov
<span title="2013-01-23">2013</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The clique tree algorithm is the standard method for doing inference in Bayesian networks. It works by manipulating clique potentials - distributions over the variables in a clique.  ...  The algorithm essentially does clique tree propagation, using approximate inference to estimate the densities in each clique.  ...  We would like to thank Ron Parr, Xavier Boyen and Si mon Tong for useful discussions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1301.6709v1">arXiv:1301.6709v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dqzgpf7gujbelkvgwgfmg5k3g4">fatcat:dqzgpf7gujbelkvgwgfmg5k3g4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200911064734/https://arxiv.org/ftp/arxiv/papers/1301/1301.6709.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/bc/41/bc4154609acb62b1fd6390a9aecdd56587e82896.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1301.6709v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>
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