Holdings: Advances in Bayesian networks

Advances in Bayesian networks:

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Bibliographic Details
Format:	Book
Language:	English
Published:	Berlin [u.a.] Springer 2004
Series:	Studies in fuzziness and soft computing 146
Subjects:	Apprentissage automatique Besliskunde Machine-learning Methode van Bayes Neurale netwerken Réseaux neuronaux (Informatique) Statistique bayésienne - Informatique Datenverarbeitung Bayesian statistical decision theory > Data processing Machine learning Neural networks (Computer science) Bayes-Netz Aufsatzsammlung
Online Access:	Inhaltsverzeichnis
Item Description:	Literaturangaben
Physical Description:	XI, 328 S. graph. Darst.
ISBN:	3540208763

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adam_text	JOSE A. GAMEZ SERAFIN MORAL ANTONIO SALMERON (EDS.) ADVANCES IN BAYESIAN NETWORKS SPRINGER CONTENTS FOUNDATIONS HYPERCAUSALITY, RANDOMISATION LOCAL AND GLOBAL INDEPENDENCE 1 ALIREZA DANESHKHAH, JIM. Q.SMITH 1 INTRODUCTION 1 2 RELATIONSHIPS BETWEEN CAUSALITY AND PARAMETER INDEPENDENCE .... 4 3 THE MULTICAUSAL ESSENTIAL GRAPH 10 4 DISCUSSION 14 REFERENCES 17 INTERFACE VERIFICATION FOR MULTIAGENT PROBABILISTIC INFERENCE .. 19 Y. XIANG, X. CHEN 1 INTRODUCTION 19 2 OVERVIEW OF MAMSBNS 20 3 THE ISSUE OF COOPERATIVE VERIFICATION 23 4 CHECKING PRIVATE PARENTS 24 5 PROCESSING PUBLIC PARENTS 25 6 COOPERATIVE VERIFICATION IN A GENERAL HYPERTREE 33 7 COMPLEXITY 34 8 ALTERNATIVE METHODS OF VERIFICATION 35 9 CONCLUSION 37 REFERENCES 38 PROPAGATION OPTIMAL TIME-SPACE TRADEOFF IN PROBABILISTIC INFERENCE 39 DAVID ALLEN, ADNAN DARWICHE 1 INTRODUCTION 39 2 ANY-SPACE INFERENCE 40 3 THE CACHE ALLOCATION PROBLEM 44 4 TIME-SPACE TRADEOFF 48 5 CONCLUSIONS 54 REFERENCES 55 HIERARCHICAL JUNCTION TREES 57 ROBERTO 0. PUCH, JIM Q. SMITH, CONCHA BIELZA 1 INTRODUCTION 57 2 BAYESIAN NETWORKS 59 3 JUNCTION TREES 61 VIII 4 FORECASTING IN THE DYNAMIC SETTING USING JUNCTION TREES 62 5 HIERARCHICAL JUNCTION TREES 64 6 CONCLUSIONS 74 REFERENCES 74 ALGORITHMS FOR APPROXIMATE PROBABILITY PROPAGATION IN BAYESIAN NETWORKS 77 ANDRES CANO, SERAFIN MORAL, ANTONIO SALMERON 1 APPROXIMATE PROBABILITY PROPAGATION 77 2 THE COMPLEXITY OF PROBABILITY PROPAGATION 78 3 THE VARIABLE ELIMINATION ALGORITHM 79 4 SHENOY-SHAFER PROPAGATION 81 5 MONTE CARLO ALGORITHMS FOR PROBABILITY PROPAGATION 88 6 CONCLUSIONS 97 REFERENCES 97 ABDUCTIVE INFERENCE IN BAYESIAN NETWORKS: A REVIEW 101 JOSE A. GAMEZ 1 INTRODUCTION 101 2 ABDUCTIVE INFERENCE IN PROBABILISTIC REASONING 102 3 SOLVING TOTAL ABDUCTION (MPE) IN BNS 105 4 SOLVING PARTIAL ABDUCTION (MAP) IN BNS 109 5 COMPLEXITY RESULTS 115 6 CONCLUSIONS 116 REFERENCES 117 INFLUENCE DIAGRAMS CAUSAL MODELS, VALUE OF INTERVENTION, AND SEARCH FOR OPPORTUNITIES 121 TSAI-CHING LU, MAREK J. DRUZDZEL 1 INTRODUCTION 121 2 CAUSAL MODELS 123 3 AUGMENTED MODELS 126 4 VALUE OF INTERVENTION 128 5 SEARCH FOR OPPORTUNITIES 130 6 NON-INTERVENING ACTION 133 7 DISCUSSION 133 REFERENCES 135 ADVANCES IN DECISION GRAPHS 137 THOMAS D. NIELSEN, FINN V. JENSEN 1 INTRODUCTION 137 2 INFLUENCE DIAGRAMS 138 3 MODELING DECISION PROBLEMS 142 IX 4 EVALUATING DECISION PROBLEMS 150 5 MODEL ANALYSIS 154 REFERENCES 156 REAL-WORLD APPLICATIONS OF INFLUENCE DIAGRAMS 161 MANUEL GOMEZ 1 DECISION MAKING UNDER UNCERTAINTY 161 2 ARTIFICIAL INTELLIGENCE, EXPERT SYSTEMS AND DECISION SUPPORT SYSTEMS 162 3 TECHNIQUES TO BUILD COMPLEX DSSS 163 4 REAL WORLD APPLICATIONS 167 5 CONCLUSIONS 173 REFERENCES 173 LEARNING LEARNING BAYESIAN NETWORKS BY FLOATING SEARCH METHODS 181 ROSA BLANCO, INAKI INZA, PEDRO LARRANAGA 1 INTRODUCTION 181 2 LEARNING BAYESIAN NETWORKS BY A SCORE+SEARCH APPROACH 182 3 FLOATING METHODS TO LEARN BAYESIAN NETWORKS STRUCTURES 187 4 EXPERIMENTAL RESULTS 191 5 CONCLUSIONS 195 REFERENCES 196 A GRAPHICAL META-MODEL FOR REASONING ABOUT BAYESIAN NETWORK STRUCTURE 201 LUIS M. DE CAMPOS, JOSE A. GAMEZ, J. MIGUEL PUERTA 1 INTRODUCTION 201 2 LEARNING BAYESIAN NETWORKS 202 3 LEARNING A GRAPHICAL META-MODEL 206 4 EXAMPLES 209 5 CONCLUDING REMARKS 212 REFERENCES 214 RESTRICTED BAYESIAN NETWORK STRUCTURE LEARNING 217 PETER J.F. LUCAS 1 INTRODUCTION 217 2 BACKGROUND 219 3 FANS: FOREST-AUGMENTED BAYESIAN NETWORKS 222 4 EVALUATION 225 5 DISCUSSION 230 REFERENCES 232 X SCALED CONJUGATE GRADIENTS FOR MAXIMUM LIKELIHOOD: AN EMPIRICAL COMPARISON WITH THE EM ALGORITHM 235 KRISTIAN KERSTING, NIELS LANDWEHR 1 INTRODUCTION 235 2 BAYESIAN NETWORKS 237 3 BASIC MAXIMUM LIKELIHOOD ESTIMATION 238 4 EM-HARD AND EM-EASY PROBLEMS 241 5 (SCALED) CONJUGATE GRADIENTS 241 6 EXPERIMENTS 244 7 RELATED WORK 252 8 CONCLUSIONS 252 REFERENCES 253 LEARNING ESSENTIAL GRAPH MARKOV MODELS FROM DATA 255 ROBERT CASTELO, MICHAEL D. PERLMAN 1 INTRODUCTION 255 2 BACKGROUND CONCEPTS 256 3 FACTORIZATION OF A MULTIVARIATE DISTRIBUTION ACCORDING TO AN ESSEN- TIAL GRAPH 257 4 BAYESIAN SCORING METRIC FOR MULTINOMIAL DATA 259 5 EQUIVALENCE WITH RESPECT TO OTHER FACTORIZATIONS 263 6 LOCAL COMPUTATIONS AND BAYES FACTORS 264 7 CONCLUDING REMARKS 267 REFERENCES 268 APPLICATIONS FAST PROPAGATION ALGORITHMS FOR SINGLY CONNECTED NETWORKS AND THEIR APPLICATIONS TO INFORMATION RETRIEVAL 271 LUIS M. DE CAMPOS, JUAN M. FERNANDEZ-LUNA, JUAN F. HUETE 1 INTRODUCTION 271 2 PRELIMINARIES: INFORMATION RETRIEVAL 273 3 THE BAYESIAN NETWORK RETRIEVAL MODEL 273 4 PROPOSALS FOR REDUCING THE PROPAGATION TIME 276 5 EXPERIMENTS AND RESULTS 279 6 RELATED WORKS 282 7 CONCLUDING REMARKS 286 REFERENCES 287 CONTINUOUS SPEECH RECOGNITION USING DYNAMIC BAYESIAN NETWORKS: A FAST DECODING ALGORITHM 289 MURAT DEVIREN, KHALID DAOUDI 1 INTRODUCTION 289 2 DYNAMIC BAYESIAN NETWORKS 290 XI 3 STRUCTURE SEARCH CLASS 294 4 LEARNING ALGORITHM 296 5 DECODING ALGORITHM 299 6 EXPERIMENTS 304 REFERENCES 307 APPLICATIONS OF BAYESIAN NETWORKS IN METEOROLOGY 309 RAFAEL CANO, CARMEN SORDO, JOSE M. GUTIERREZ 1 INTRODUCTION 309 2 AREA OF STUDY AND AVAILABLE DATA 310 3 SOME COMMON PROBLEMS IN METEOROLOGY 312 4 BAYESIAN NETWORKS. LEARNING FROM DATA 314 5 APPLICATIONS OF BAYESIAN NETWORKS 320 REFERENCES 327
adam_txt	JOSE A. GAMEZ SERAFIN MORAL ANTONIO SALMERON (EDS.) ADVANCES IN BAYESIAN NETWORKS SPRINGER CONTENTS FOUNDATIONS HYPERCAUSALITY, RANDOMISATION LOCAL AND GLOBAL INDEPENDENCE 1 ALIREZA DANESHKHAH, JIM. Q.SMITH 1 INTRODUCTION 1 2 RELATIONSHIPS BETWEEN CAUSALITY AND PARAMETER INDEPENDENCE . 4 3 THE MULTICAUSAL ESSENTIAL GRAPH 10 4 DISCUSSION 14 REFERENCES 17 INTERFACE VERIFICATION FOR MULTIAGENT PROBABILISTIC INFERENCE . 19 Y. XIANG, X. CHEN 1 INTRODUCTION 19 2 OVERVIEW OF MAMSBNS 20 3 THE ISSUE OF COOPERATIVE VERIFICATION 23 4 CHECKING PRIVATE PARENTS 24 5 PROCESSING PUBLIC PARENTS 25 6 COOPERATIVE VERIFICATION IN A GENERAL HYPERTREE 33 7 COMPLEXITY 34 8 ALTERNATIVE METHODS OF VERIFICATION 35 9 CONCLUSION 37 REFERENCES 38 PROPAGATION OPTIMAL TIME-SPACE TRADEOFF IN PROBABILISTIC INFERENCE 39 DAVID ALLEN, ADNAN DARWICHE 1 INTRODUCTION 39 2 ANY-SPACE INFERENCE 40 3 THE CACHE ALLOCATION PROBLEM 44 4 TIME-SPACE TRADEOFF 48 5 CONCLUSIONS 54 REFERENCES 55 HIERARCHICAL JUNCTION TREES 57 ROBERTO 0. PUCH, JIM Q. SMITH, CONCHA BIELZA 1 INTRODUCTION 57 2 BAYESIAN NETWORKS 59 3 JUNCTION TREES 61 VIII 4 FORECASTING IN THE DYNAMIC SETTING USING JUNCTION TREES 62 5 HIERARCHICAL JUNCTION TREES 64 6 CONCLUSIONS 74 REFERENCES 74 ALGORITHMS FOR APPROXIMATE PROBABILITY PROPAGATION IN BAYESIAN NETWORKS 77 ANDRES CANO, SERAFIN MORAL, ANTONIO SALMERON 1 APPROXIMATE PROBABILITY PROPAGATION 77 2 THE COMPLEXITY OF PROBABILITY PROPAGATION 78 3 THE VARIABLE ELIMINATION ALGORITHM 79 4 SHENOY-SHAFER PROPAGATION 81 5 MONTE CARLO ALGORITHMS FOR PROBABILITY PROPAGATION 88 6 CONCLUSIONS 97 REFERENCES 97 ABDUCTIVE INFERENCE IN BAYESIAN NETWORKS: A REVIEW 101 JOSE A. GAMEZ 1 INTRODUCTION 101 2 ABDUCTIVE INFERENCE IN PROBABILISTIC REASONING 102 3 SOLVING TOTAL ABDUCTION (MPE) IN BNS 105 4 SOLVING PARTIAL ABDUCTION (MAP) IN BNS 109 5 COMPLEXITY RESULTS 115 6 CONCLUSIONS 116 REFERENCES 117 INFLUENCE DIAGRAMS CAUSAL MODELS, VALUE OF INTERVENTION, AND SEARCH FOR OPPORTUNITIES 121 TSAI-CHING LU, MAREK J. DRUZDZEL 1 INTRODUCTION 121 2 CAUSAL MODELS 123 3 AUGMENTED MODELS 126 4 VALUE OF INTERVENTION 128 5 SEARCH FOR OPPORTUNITIES 130 6 NON-INTERVENING ACTION 133 7 DISCUSSION 133 REFERENCES 135 ADVANCES IN DECISION GRAPHS 137 THOMAS D. NIELSEN, FINN V. JENSEN 1 INTRODUCTION 137 2 INFLUENCE DIAGRAMS 138 3 MODELING DECISION PROBLEMS 142 IX 4 EVALUATING DECISION PROBLEMS 150 5 MODEL ANALYSIS 154 REFERENCES 156 REAL-WORLD APPLICATIONS OF INFLUENCE DIAGRAMS 161 MANUEL GOMEZ 1 DECISION MAKING UNDER UNCERTAINTY 161 2 ARTIFICIAL INTELLIGENCE, EXPERT SYSTEMS AND DECISION SUPPORT SYSTEMS 162 3 TECHNIQUES TO BUILD COMPLEX DSSS 163 4 REAL WORLD APPLICATIONS 167 5 CONCLUSIONS 173 REFERENCES 173 LEARNING LEARNING BAYESIAN NETWORKS BY FLOATING SEARCH METHODS 181 ROSA BLANCO, INAKI INZA, PEDRO LARRANAGA 1 INTRODUCTION 181 2 LEARNING BAYESIAN NETWORKS BY A SCORE+SEARCH APPROACH 182 3 FLOATING METHODS TO LEARN BAYESIAN NETWORKS STRUCTURES 187 4 EXPERIMENTAL RESULTS 191 5 CONCLUSIONS 195 REFERENCES 196 A GRAPHICAL META-MODEL FOR REASONING ABOUT BAYESIAN NETWORK STRUCTURE 201 LUIS M. DE CAMPOS, JOSE A. GAMEZ, J. MIGUEL PUERTA 1 INTRODUCTION 201 2 LEARNING BAYESIAN NETWORKS 202 3 LEARNING A GRAPHICAL META-MODEL 206 4 EXAMPLES 209 5 CONCLUDING REMARKS 212 REFERENCES 214 RESTRICTED BAYESIAN NETWORK STRUCTURE LEARNING 217 PETER J.F. LUCAS 1 INTRODUCTION 217 2 BACKGROUND 219 3 FANS:' FOREST-AUGMENTED BAYESIAN NETWORKS 222 4 EVALUATION 225 5 DISCUSSION 230 REFERENCES 232 X SCALED CONJUGATE GRADIENTS FOR MAXIMUM LIKELIHOOD: AN EMPIRICAL COMPARISON WITH THE EM ALGORITHM 235 KRISTIAN KERSTING, NIELS LANDWEHR 1 INTRODUCTION 235 2 BAYESIAN NETWORKS 237 3 BASIC MAXIMUM LIKELIHOOD ESTIMATION 238 4 "EM-HARD" AND "EM-EASY" PROBLEMS 241 5 (SCALED) CONJUGATE GRADIENTS 241 6 EXPERIMENTS 244 7 RELATED WORK 252 8 CONCLUSIONS 252 REFERENCES 253 LEARNING ESSENTIAL GRAPH MARKOV MODELS FROM DATA 255 ROBERT CASTELO, MICHAEL D. PERLMAN 1 INTRODUCTION 255 2 BACKGROUND CONCEPTS 256 3 FACTORIZATION OF A MULTIVARIATE DISTRIBUTION ACCORDING TO AN ESSEN- TIAL GRAPH 257 4 BAYESIAN SCORING METRIC FOR MULTINOMIAL DATA 259 5 EQUIVALENCE WITH RESPECT TO OTHER FACTORIZATIONS 263 6 LOCAL COMPUTATIONS AND BAYES FACTORS 264 7 CONCLUDING REMARKS 267 REFERENCES 268 APPLICATIONS FAST PROPAGATION ALGORITHMS FOR SINGLY CONNECTED NETWORKS AND THEIR APPLICATIONS TO INFORMATION RETRIEVAL 271 LUIS M. DE CAMPOS, JUAN M. FERNANDEZ-LUNA, JUAN F. HUETE 1 INTRODUCTION 271 2 PRELIMINARIES: INFORMATION RETRIEVAL 273 3 THE BAYESIAN NETWORK RETRIEVAL MODEL 273 4 PROPOSALS FOR REDUCING THE PROPAGATION TIME 276 5 EXPERIMENTS AND RESULTS 279 6 RELATED WORKS 282 7 CONCLUDING REMARKS ' 286 REFERENCES 287 CONTINUOUS SPEECH RECOGNITION USING DYNAMIC BAYESIAN NETWORKS: A FAST DECODING ALGORITHM 289 MURAT DEVIREN, KHALID DAOUDI 1 INTRODUCTION 289 2 DYNAMIC BAYESIAN NETWORKS 290 XI 3 STRUCTURE SEARCH CLASS 294 4 LEARNING ALGORITHM 296 5 DECODING ALGORITHM 299 6 EXPERIMENTS 304 REFERENCES 307 APPLICATIONS OF BAYESIAN NETWORKS IN METEOROLOGY 309 RAFAEL CANO, CARMEN SORDO, JOSE M. GUTIERREZ 1 INTRODUCTION 309 2 AREA OF STUDY AND AVAILABLE DATA 310 3 SOME COMMON PROBLEMS IN METEOROLOGY 312 4 BAYESIAN NETWORKS. LEARNING FROM DATA 314 5 APPLICATIONS OF BAYESIAN NETWORKS 320 REFERENCES 327
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spelling	Advances in Bayesian networks José A. Gámez ... (eds.) Berlin [u.a.] Springer 2004 XI, 328 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Studies in fuzziness and soft computing 146 Literaturangaben Apprentissage automatique Besliskunde gtt Machine-learning gtt Methode van Bayes gtt Neurale netwerken gtt Réseaux neuronaux (Informatique) Statistique bayésienne - Informatique Datenverarbeitung Bayesian statistical decision theory Data processing Machine learning Neural networks (Computer science) Bayes-Netz (DE-588)4567228-3 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Bayes-Netz (DE-588)4567228-3 s DE-604 Gámez, José A. Sonstige oth Studies in fuzziness and soft computing 146 (DE-604)BV021858135 146 HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015628917&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Advances in Bayesian networks Studies in fuzziness and soft computing Apprentissage automatique Besliskunde gtt Machine-learning gtt Methode van Bayes gtt Neurale netwerken gtt Réseaux neuronaux (Informatique) Statistique bayésienne - Informatique Datenverarbeitung Bayesian statistical decision theory Data processing Machine learning Neural networks (Computer science) Bayes-Netz (DE-588)4567228-3 gnd
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title	Advances in Bayesian networks
title_auth	Advances in Bayesian networks
title_exact_search	Advances in Bayesian networks
title_exact_search_txtP	Advances in Bayesian networks
title_full	Advances in Bayesian networks José A. Gámez ... (eds.)
title_fullStr	Advances in Bayesian networks José A. Gámez ... (eds.)
title_full_unstemmed	Advances in Bayesian networks José A. Gámez ... (eds.)
title_short	Advances in Bayesian networks
title_sort	advances in bayesian networks
topic	Apprentissage automatique Besliskunde gtt Machine-learning gtt Methode van Bayes gtt Neurale netwerken gtt Réseaux neuronaux (Informatique) Statistique bayésienne - Informatique Datenverarbeitung Bayesian statistical decision theory Data processing Machine learning Neural networks (Computer science) Bayes-Netz (DE-588)4567228-3 gnd
topic_facet	Apprentissage automatique Besliskunde Machine-learning Methode van Bayes Neurale netwerken Réseaux neuronaux (Informatique) Statistique bayésienne - Informatique Datenverarbeitung Bayesian statistical decision theory Data processing Machine learning Neural networks (Computer science) Bayes-Netz Aufsatzsammlung
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015628917&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
volume_link	(DE-604)BV021858135
work_keys_str_mv	AT gamezjosea advancesinbayesiannetworks

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