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141027s1994 caua ob 101 0 eng d |
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|a OPELS
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|a 893875044
|a 898422487
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|a 9781483298603
|q (electronic bk.)
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|a 1483298604
|q (electronic bk.)
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|z 1558603328
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|a Conference on Uncertainty in Artificial Intelligence
|n (10th :
|d 1994 :
|c University of Washington)
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| 245 |
1 |
0 |
|a Uncertainty in artificial intelligence :
|b proceedings of the Tenth Conference (1994) : July 29-31, 1994 /
|c Tenth Conference on Uncertainty in Artificial Intelligence, University of Washington, Seattle ; edited by Ramon Lopez de Mantaras, David Poole.
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| 264 |
|
1 |
|a San Francisco, Calif. :
|b Morgan Kaufmann Publishers,
|c [1994]
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| 264 |
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4 |
|c �1994
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| 300 |
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|a 1 online resource (vi, 616 pages) :
|b illustrations
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 504 |
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|a Includes bibliographical references and index.
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0 |
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|a Print version record.
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|a Front Cover; Uncertainty in Artificial Intelligence; Copyright Page; Table of Contents; Preface; Acknowledgments; Chapter 1. Ending-based Strategies for Part-of-speech Tagging; Abstract; 1 INTRODUCTION; 2 BACKGROUND; 3 THE EXPERIMENTS; 4 RESULTS; 5 DISCUSSION AND FUTUREWORK; Acknowledgments; References; Chapter 2. An evaluation of an algorithm for inductive learning of Bayesian belief networks using simulated data sets; Abstract; 1 INTRODUCTION; 2 METHODS; 3 RESULTS; 4 CONCLUSIONS; Acknowledgements; References; Appendix I.
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| 505 |
8 |
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|a Chapter 3. Probabilistic Constraint Satisfaction with Non-Gaussian NoiseAbstract; 1 INTRODUCTION; 2 MULTICOMPONENT ALGORITHM; 3. EXPERIMENTS AND RESULTS; 4 DISCUSSION; 5 RELATED WORK; 6 CONCLUSIONS; Acknowledgments; References; Chapter 4. A Bayesian Method Reexamined; Abstract; 1 INTRODUCTION; 2 THE K2 METRIC; 3 EXAMPLES AND DISCUSSION; 4 ANALYSIS; 5 CONCLUSION; Acknowledgments; References; Chapter 5. Laplace's Method Approximations for Probabilistic Inference in Belief Networks with Continuous Variables; Abstract; 1 Introduction.
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| 505 |
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|a 2 Laplace's Method and Approximations for Probabilistic Inference3 Implementation Issues and Limitations; 4 An Application to a Medical Inference Problem; 5 Final Considerations; Acknowlegements; References; Chapter 6. Generating New Beliefs From Old; Abstract; 1 Introduction; 2 Technical preliminaries; 3 The three methods; 4 Discussion; References; Chapter 7. Counterfactual Probabilities: Computational Methods, Bounds and Applications; Abstract; 1 INTRODUCTION; 2 NOTATION; 3 BOUNDS ONCOUNTERFACTUALS; 4 APPLICATION TO CLINICAL TRIALS WITH IMPERFECT COMPLIANCE.
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| 505 |
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|6 880-01
|a 4 Experimental ResultsRemark.; References; Chapter 10. Possibility and necessity functions over non-classical logics; Abstract; 1 Introduction; 2 Non-classical necessity and possibility functions; 3 Application to reasoning with uncertain and inconsistent information; 4 Conclusion; 5 References; Chapter 11. Exploratory Model Building; Abstract; 1 Introduction; 2 The Scenario-Building Process; 3 Probabilistic Knowledge; 4 The Dependency Relation; 5 Structure of an Imagined Context; 6 Constructing Preferred Contexts; 7 Conclusion; Acknowledgment; References.
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| 650 |
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0 |
|a Uncertainty (Information theory)
|v Congresses.
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| 650 |
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0 |
|a Artificial intelligence
|v Congresses.
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| 650 |
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6 |
|a Incertitude (Th�eorie de l'information)
|0 (CaQQLa)201-0003328
|v Congr�es.
|0 (CaQQLa)201-0378219
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| 650 |
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6 |
|a Intelligence artificielle
|0 (CaQQLa)201-0008626
|v Congr�es.
|0 (CaQQLa)201-0378219
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| 650 |
|
7 |
|a COMPUTERS
|x General.
|2 bisacsh
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| 650 |
|
7 |
|a Artificial intelligence
|2 fast
|0 (OCoLC)fst00817247
|
| 650 |
|
7 |
|a Uncertainty (Information theory)
|2 fast
|0 (OCoLC)fst01160838
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| 655 |
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2 |
|a Congress
|0 (DNLM)D016423
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| 655 |
|
7 |
|a proceedings (reports)
|2 aat
|0 (CStmoGRI)aatgf300027316
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| 655 |
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7 |
|a Conference papers and proceedings
|2 fast
|0 (OCoLC)fst01423772
|
| 655 |
|
7 |
|a Conference papers and proceedings.
|2 lcgft
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| 655 |
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7 |
|a Actes de congr�es.
|2 rvmgf
|0 (CaQQLa)RVMGF-000001049
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| 700 |
1 |
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|a L�opez de M�antaras, Ramon,
|d 1952-
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| 700 |
1 |
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|a Poole, David L.
|q (David Lynton),
|d 1958-
|
| 776 |
0 |
8 |
|i Print version:
|a Conference on Uncertainty in Artificial Intelligence (10th : 1994 : University of Washington).
|t Uncertainty in artificial intelligence
|z 1558603328
|w (OCoLC)31251390
|
| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9781558603325
|z Texto completo
|
| 880 |
8 |
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|6 505-01/(S
|a 5 APPLICATIONS TO LIABILITY JUDGMENT6 CONCLUSION; Acknowledgements; References; Chapter 8. Modus Ponens Generating Function in the Class of Λ-valuations of Plausibility; Abstract; 1 STABILITY OF DECISIONS IN INFERENCE PROCEDURES; 2 STRICT MONOTONICITY OF CONCLUSIONS; 3 Λ-VALUATIONS OF PLAUSIBILITY; 4 NEGATION OPERATION ON F; 5 MODUS PONENS GENERATING FUNCTIONS ON F; 6 EXAMPLE AND APPLICATIONS; Acknowledgements; References; Chapter 9. Approximation Algorithms for the Loop Cutset Problem; Abstract; 1 Introduction; 2 The Loop Cutset Problem; 3 Algorithms For The WVFS problem.
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