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Probabilistic Logics and Probabilistic Networks

E-BookPDF1 - PDF WatermarkE-Book
155 Seiten
Englisch
Springer Netherlandserschienen am19.11.20102011
While probabilistic logics in principle might be applied to solve a range of problems, in practice they are rarely applied - perhaps because they seem disparate, complicated, and computationally intractable. This programmatic book argues that several approaches to probabilistic logic fit into a simple unifying framework in which logically complex evidence is used to associate probability intervals or probabilities with sentences. Specifically, Part I shows that there is a natural way to present a question posed in probabilistic logic, and that various inferential procedures provide semantics for that question, while Part II shows that there is the potential to develop computationally feasible methods to mesh with this framework. The book is intended for researchers in philosophy, logic, computer science and statistics. A familiarity with mathematical concepts and notation is presumed, but no advanced knowledge of logic or probability theory is required.

Rolf Haenni is professor at the Department of Engineering and Information Technology of the University of Applied Sciences of Berne (BFH-TI) in Biel, Switzerland. He holds a PhD degree in Computer Science from the University of Fribourg, for which he received the prize for the best thesis in 1996. Jan-Willem Romeijn is an assistant professor at the Philosophy Faculty of the University of Groningen. He obtained degrees cum laude in both physics and philosophy, worked as a financial mathematician and received his doctorate cum laude from the University of Groningen in 2005. Gregory Wheeler is Senior Research Scientist at the Centre for Artificial Intelligence at the New University of Lisbon. He received a joint PhD in Philosophy and Computer Science from the University of Rochester in 2002. Jon Williamson is Professor of Reasoning, Inference and Scientific Method at the University of Kent. He completed his PhD in Philosophy in 1998 and in 2007 was Times Higher Education UK Young Researcher of the Year.mehr
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Produkt

KlappentextWhile probabilistic logics in principle might be applied to solve a range of problems, in practice they are rarely applied - perhaps because they seem disparate, complicated, and computationally intractable. This programmatic book argues that several approaches to probabilistic logic fit into a simple unifying framework in which logically complex evidence is used to associate probability intervals or probabilities with sentences. Specifically, Part I shows that there is a natural way to present a question posed in probabilistic logic, and that various inferential procedures provide semantics for that question, while Part II shows that there is the potential to develop computationally feasible methods to mesh with this framework. The book is intended for researchers in philosophy, logic, computer science and statistics. A familiarity with mathematical concepts and notation is presumed, but no advanced knowledge of logic or probability theory is required.

Rolf Haenni is professor at the Department of Engineering and Information Technology of the University of Applied Sciences of Berne (BFH-TI) in Biel, Switzerland. He holds a PhD degree in Computer Science from the University of Fribourg, for which he received the prize for the best thesis in 1996. Jan-Willem Romeijn is an assistant professor at the Philosophy Faculty of the University of Groningen. He obtained degrees cum laude in both physics and philosophy, worked as a financial mathematician and received his doctorate cum laude from the University of Groningen in 2005. Gregory Wheeler is Senior Research Scientist at the Centre for Artificial Intelligence at the New University of Lisbon. He received a joint PhD in Philosophy and Computer Science from the University of Rochester in 2002. Jon Williamson is Professor of Reasoning, Inference and Scientific Method at the University of Kent. He completed his PhD in Philosophy in 1998 and in 2007 was Times Higher Education UK Young Researcher of the Year.
Details
Weitere ISBN/GTIN9789400700086
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2010
Erscheinungsdatum19.11.2010
Auflage2011
ReiheSynthese Library
Reihen-Nr.350
Seiten155 Seiten
SpracheEnglisch
IllustrationenXIII, 155 p.
Artikel-Nr.1711687
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
1;Preface;7
2;Acknowledgements;8
3;Contents;9
4;Part I Probabilistic Logics;12
4.1;1 Introduction;13
4.1.1;1.1 The Fundamental Question of Probabilistic Logic;13
4.1.2;1.2 The Potential of Probabilistic Logic;14
4.1.3;1.3 Overview of the Book;15
4.1.4;1.4 Philosophical and Historical Background;17
4.1.5;1.5 Notation and Formal Setting;19
4.2;2 Standard Probabilistic Semantics;21
4.2.1;2.1 Background;21
4.2.1.1;2.1.1 Kolmogorov Probabilities;22
4.2.1.2;2.1.2 Interval-Valued Probabilities;23
4.2.1.3;2.1.3 Imprecise Probabilities;25
4.2.1.4;2.1.4 Convexity;26
4.2.2;2.2 Representation;28
4.2.3;2.3 Interpretation;29
4.3;3 Probabilistic Argumentation;31
4.3.1;3.1 Background;32
4.3.2;3.2 Representation;35
4.3.3;3.3 Interpretation;36
4.3.3.1;3.3.1 Generalizing the Standard Semantics;36
4.3.3.2;3.3.2 Premises from Unreliable Sources;38
4.4;4 Evidential Probability;42
4.4.1;4.1 Background;42
4.4.1.1;4.1.1 Calculating Evidential Probability;46
4.4.1.2;4.1.2 Extended Example: When Pigs Die;49
4.4.2;4.2 Representation;53
4.4.3;4.3 Interpretation;53
4.4.3.1;4.3.1 First-order Evidential Probability;54
4.4.3.2;4.3.2 Counterfactual Evidential Probability;55
4.4.3.3;4.3.3 Second-Order Evidential Probability;55
4.5;5 Statistical Inference;58
4.5.1;5.1 Background;58
4.5.1.1;5.1.1 Classical Statistics as Inference?;58
4.5.1.2;5.1.2 Fiducial Probability;61
4.5.1.3;5.1.3 Evidential Probability and Direct Inference;64
4.5.2;5.2 Representation;66
4.5.2.1;5.2.1 Fiducial Probability;66
4.5.2.2;5.2.2 Evidential Probability and the Fiducial Argument;67
4.5.3;5.3 Interpretation;68
4.5.3.1;5.3.1 Fiducial Probability;68
4.5.3.2;5.3.2 Evidential Probability;69
4.6;6 Bayesian Statistical Inference;71
4.6.1;6.1 Background;71
4.6.2;6.2 Representation;73
4.6.2.1;6.2.1 Infinitely Many Hypotheses;74
4.6.2.2;6.2.2 Interval-Valued Priors and Posteriors;76
4.6.3;6.3 Interpretation;77
4.6.3.1;6.3.1 Interpretation of Probabilities;77
4.6.3.2;6.3.2 Bayesian Confidence Intervals;78
4.7;7 Objective Bayesian Epistemology;80
4.7.1;7.1 Background;80
4.7.1.1;7.1.1 Determining Objective Bayesian Degrees of Belief;81
4.7.1.2;7.1.2 Constraints on Degrees of Belief;82
4.7.1.3;7.1.3 Propositional Languages;83
4.7.1.4;7.1.4 Predicate Languages;84
4.7.1.5;7.1.5 Objective Bayesianism in Perspective;86
4.7.2;7.2 Representation;87
4.7.3;7.3 Interpretation;87
5;Part II Probabilistic Networks;90
5.1;8 Credal and Bayesian Networks;91
5.1.1;8.1 Kinds of Probabilistic Network;92
5.1.1.1;8.1.1 Extensions;93
5.1.1.2;8.1.2 Extensions and Coordinates;94
5.1.1.3;8.1.3 Parameterised Credal Networks;96
5.1.2;8.2 Algorithms for Probabilistic Networks;97
5.1.2.1;8.2.1 Requirements of the Probabilistic Logic Framework;97
5.1.2.2;8.2.2 Compiling Probabilistic Networks;98
5.1.2.3;8.2.3 The Hill-Climbing Algorithm for Credal Networks;100
5.1.2.4;8.2.4 Complex Queries and Parameterised Credal Networks;102
5.2;9 Networks for the Standard Semantics;104
5.2.1;9.1 The Poverty of Standard Semantics;104
5.2.2;9.2 Constructing a Credal Net;105
5.2.3;9.3 Dilation and Independence;109
5.3;10 Networks for Probabilistic Argumentation ;111
5.3.1;10.1 Probabilistic Argumentation with Credal Sets;111
5.3.2;10.2 Constructing and Applying the Credal Network;112
5.4;11 Networks for Evidential Probability;115
5.4.1;11.1 First-Order Evidential Probability;115
5.4.2;11.2 Second-Order Evidential Probability;117
5.4.3;11.3 Chaining Inferences;120
5.5;12 Networks for Statistical Inference;122
5.5.1;12.1 Functional Models and Networks;122
5.5.1.1;12.1.1 Capturing the Fiducial Argument in a Network;122
5.5.1.2;12.1.2 Aiding Fiducial Inference with Networks;123
5.5.1.3;12.1.3 Trouble with Step-by-Step Fiducial Probability;125
5.5.2;12.2 Evidential Probability and the Fiducial Argument;126
5.5.2.1;12.2.1 First-Order EP and the Fiducial Argument;126
5.5.2.2;12.2.2 Second-Order EP and the Fiducial Argument;127
5.6;13 Networks for Bayesian Statistical Inference;128
5.6.1;13.1 Credal Networks as Statistical Hypotheses;128
5.6.1.1;13.1.1 Construction of the Credal Network;129
5.6.1.2;13.1.2 Computational Advantages of Using the Credal Network;130
5.6.2;13.2 Extending Statistical Inference with Credal Networks;131
5.6.2.1;13.2.1 Interval-Valued Likelihoods;132
5.6.2.2;13.2.2 Logically Complex Statements with Statistical Hypotheses;134
5.7;14 Networks for Objective Bayesianism ;135
5.7.1;14.1 Propositional Languages;135
5.7.2;14.2 Predicate Languages;137
5.8;15 Conclusion;140
6;References;141
7;Index;152mehr

Autor

Rolf Haenni is professor at the Department of Engineering and Information Technology of the University of Applied Sciences of Berne (BFH-TI) in Biel, Switzerland. He holds a PhD degree in Computer Science from the University of Fribourg, for which he received the prize for the best thesis in 1996. Jan-Willem Romeijn is an assistant professor at the Philosophy Faculty of the University of Groningen. He obtained degrees cum laude in both physics and philosophy, worked as a financial mathematician and received his doctorate cum laude from the University of Groningen in 2005. Gregory Wheeler is Senior Research Scientist at the Centre for Artificial Intelligence at the New University of Lisbon. He received a joint PhD in Philosophy and Computer Science from the University of Rochester in 2002. Jon Williamson is Professor of Reasoning, Inference and Scientific Method at the University of Kent. He completed his PhD in Philosophy in 1998 and in 2007 was Times Higher Education UK Young Researcher of the Year.
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