Advances in Bayesian networks:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2004
|
| Schriftenreihe: | Studies in fuzziness and soft computing
146 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben |
| Beschreibung: | XI, 328 S. graph. Darst. |
| ISBN: | 3540208763 |
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Datensatz im Suchindex
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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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| index_date | 2024-07-02T17:25:33Z |
| indexdate | 2024-07-09T20:57:13Z |
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| isbn | 3540208763 |
| language | English |
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| physical | XI, 328 S. graph. Darst. |
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| series | Studies in fuzziness and soft computing |
| series2 | Studies in fuzziness and soft computing |
| 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 |
| subject_GND | (DE-588)4567228-3 (DE-588)4143413-4 |
| 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 |