Bayesian nets and causality: philosophical and computational foundations
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Oxford [u.a.]
Oxford Univ. Press
2005
|
| Edition: | 1. publ. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Item Description: | Includes bibliographical references (p. 219-234) and index |
| Physical Description: | IX, 239 S. graph. Darst. 24 cm |
| ISBN: | 019853079X |
Staff View
MARC
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| 245 | 1 | 0 | |a Bayesian nets and causality |b philosophical and computational foundations |c Jon Williamson |
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Record in the Search Index
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| adam_text | Introduction
1.1
Philosophical Claims
1.2
Computational Claims
Probability
2.1
Variables
2.2
Probability Functions
2.3
Interpretations and Distinctions
2.4
Frequency-
2.5
Propensity
2.6
Chance
2.7
Bayesianism
2.8
Chance as Ultimate Belief
2.9
Applying Probability
CONTENTS
ι
1
2
4
4
5
7
7
9
10
11
12
13
Bayesian Nets
14
3.1
Bayesian Networks
14
3.2
Independence and D-Separation
16
3.3
Representing Probability Functions
17
3.4
Inference in Bayesian Nets
20
3.5
Constructing Bayesian Nets
21
3.6
The Adding-Arrows Algorithm
24
3.7
Adding Arrows: an Example
26
3.8
The Approximation Subspace
30
3.9
Greed of Adding Arrows
38
3.10
Complexity of Adding Arrows
43
3.11
The Case for Adding Arrows
48
Causal Nets: Foundational Problems
49
4.1
Causally Interpreted Bayesian Nets
49
4.2
Physical Causality, Physical Probability
51
4.3
Mental Causality, Physical Probability
57
4.4
Physical Causality, Mental Probability
62
4.5
Mental Causality, Mental Probability
63
Objective Bayesianism
65
5.1
Objective versus Subjective
65
5.2
The Origins of Objective Bayesianism
66
5.3
Empirical Constraints: The Calibration Principle
70
5.4
Logical Constraints: The Maximum Entropy Principle
79
5.5
Maximising Entropy Efficiently
84
vu
viii CONTENTS
5.6
From Constraints to Markov Network
86
5.7
From Markov to Bayesian Network
89
5.8
Causal Constraints
95
6
Two-Stage Bayesian Nets
107
6.1
Causal Nets Maximise Entropy
107
6.2
Refining Bayesian Nets
108
6.3
A Two-Stage Methodology
108
7
Causality
110
7.1
Metaphysics of Causality
110
7.2
Mechanisms 111
7.3
Probabilistic Causality
112
7.4
Counterfactuals
115
7.5
Agency
116
8
Discovering Causal Relationships
118
8.1
Epistemology of Causality
118
8.2
Hypothetico-Deductive Discovery
118
8.3
Inductive Learning
120
8.4
Constraint-Based Induction
123
8.5
Bayesian Induction
125
8.6
Information-Theoretic Induction
125
8.7
Sharer s Causal Conjecturing
127
8.8
The Devil and the Deep Blue Sea
129
9
Epistemic
Causality
130
9.1
Mental yet Objective
130
9.2
Kant
131
9.3
Ramsey
133
9.4
The Convenience of Causality
135
9.5
Causal Beliefs
138
9.6
Special Cases
140
9.7
Uniqueness and Objectivity
143
9.8
Causal Knowledge
146
9.9
Discovering Causal Relationships: A Synthesis
148
9.10
The Analogy with Objective Bayesianism
150
10
Recursive Causality
152
10.1
Overview
152
10.2
Causal Relations as Causes
152
10.3
Extension to Recursive Causality
155
10.4
Consistency
157
10.5
Joint Distributions
165
10.6
Related Proposals
169
10.7
Structural Equation Models
171
CONTENTS ix
10.8 Argumentation Networks 172
11 Logic 175
11.1
Overview
175
11.2 Propositional Logic 175
11.3 Bayesian
Nets for Logical Reasoning
176
11.4
Influence Relations
177
11.5
Recursive Logical Nets
180
11.6
The Effectiveness of Logical Nets
181
11.7
Logic Programming and Logical Nets
183
11.8
Logical Constraints and Logical Beliefs
185
11.9
Probability Logic
186
ll.lOPartial Entailment
187
11.11
Semantics for Probability Logic
191
11.12Deciding Probabilistic Entailment
192
12
Language Change
194
12.1
Two Problems of Belief Change
194
12.2
Language Contains Implicit Knowledge
196
12.3
Goodman s New Problem of Induction
197
12.4
The Principle of Indifference
199
12.5
Indirect Evidence
200
12.6
Types of Language Change
201
12.7
Conservativity
202
12.8
Prospects for a Solution
207
12.9
Language Change Update Strategies
208
12.10
The
Maximin
Update Strategy
209
12.11
Cross Entropy Updating of Bayesian Nets
211
12.12
Compatibility and Indirect Evidence
216
12.13The Maxent Update Strategy
217
References
219
Index
235
Bayesian
nets are widely used in artificial intelligence as a calculus for
causal reasoning, enabling machines to make predictions, perform diag¬
noses, take decisions and even to discover causal relationships. This book,
aimed at researchers and graduate students in computer science, mathe¬
matics and philosophy, brings together two important research topics:
how to automate reasoning in artificial intelligence, and the nature of
causality and probability in philosophy.
|
| adam_txt |
Introduction
1.1
Philosophical Claims
1.2
Computational Claims
Probability
2.1
Variables
2.2
Probability Functions
2.3
Interpretations and Distinctions
2.4
Frequency-
2.5
Propensity
2.6
Chance
2.7
Bayesianism
2.8
Chance as Ultimate Belief
2.9
Applying Probability
CONTENTS
ι
1
2
4
4
5
7
7
9
10
11
12
13
Bayesian Nets
14
3.1
Bayesian Networks
14
3.2
Independence and D-Separation
16
3.3
Representing Probability Functions
17
3.4
Inference in Bayesian Nets
20
3.5
Constructing Bayesian Nets
21
3.6
The Adding-Arrows Algorithm
24
3.7
Adding Arrows: an Example
26
3.8
The Approximation Subspace
30
3.9
Greed of Adding Arrows
38
3.10
Complexity of Adding Arrows
43
3.11
The Case for Adding Arrows
48
Causal Nets: Foundational Problems
49
4.1
Causally Interpreted Bayesian Nets
49
4.2
Physical Causality, Physical Probability
51
4.3
Mental Causality, Physical Probability
57
4.4
Physical Causality, Mental Probability
62
4.5
Mental Causality, Mental Probability
63
Objective Bayesianism
65
5.1
Objective versus Subjective
65
5.2
The Origins of Objective Bayesianism
66
5.3
Empirical Constraints: The Calibration Principle
70
5.4
Logical Constraints: The Maximum Entropy Principle
79
5.5
Maximising Entropy Efficiently
84
vu
viii CONTENTS
5.6
From Constraints to Markov Network
86
5.7
From Markov to Bayesian Network
89
5.8
Causal Constraints
95
6
Two-Stage Bayesian Nets
107
6.1
Causal Nets Maximise Entropy
107
6.2
Refining Bayesian Nets
108
6.3
A Two-Stage Methodology
108
7
Causality
110
7.1
Metaphysics of Causality
110
7.2
Mechanisms 111
7.3
Probabilistic Causality
112
7.4
Counterfactuals
115
7.5
Agency
116
8
Discovering Causal Relationships
118
8.1
Epistemology of Causality
118
8.2
Hypothetico-Deductive Discovery
118
8.3
Inductive Learning
120
8.4
Constraint-Based Induction
123
8.5
Bayesian Induction
125
8.6
Information-Theoretic Induction
125
8.7
Sharer's Causal Conjecturing
127
8.8
The Devil and the Deep Blue Sea
129
9
Epistemic
Causality
130
9.1
Mental yet Objective
130
9.2
Kant
131
9.3
Ramsey
133
9.4
The Convenience of Causality
135
9.5
Causal Beliefs
138
9.6
Special Cases
140
9.7
Uniqueness and Objectivity
143
9.8
Causal Knowledge
146
9.9
Discovering Causal Relationships: A Synthesis
148
9.10
The Analogy with Objective Bayesianism
150
10
Recursive Causality
152
10.1
Overview
152
10.2
Causal Relations as Causes
152
10.3
Extension to Recursive Causality
155
10.4
Consistency
157
10.5
Joint Distributions
165
10.6
Related Proposals
169
10.7
Structural Equation Models
171
CONTENTS ix
10.8 Argumentation Networks 172
11 Logic 175
11.1
Overview
175
11.2 Propositional Logic 175
11.3 Bayesian
Nets for Logical Reasoning
176
11.4
Influence Relations
177
11.5
Recursive Logical Nets
180
11.6
The Effectiveness of Logical Nets
181
11.7
Logic Programming and Logical Nets
183
11.8
Logical Constraints and Logical Beliefs
185
11.9
Probability Logic
186
ll.lOPartial Entailment
187
11.11
Semantics for Probability Logic
191
11.12Deciding Probabilistic Entailment
192
12
Language Change
194
12.1
Two Problems of Belief Change
194
12.2
Language Contains Implicit Knowledge
196
12.3
Goodman's New Problem of Induction
197
12.4
The Principle of Indifference
199
12.5
Indirect Evidence
200
12.6
Types of Language Change
201
12.7
Conservativity
202
12.8
Prospects for a Solution
207
12.9
Language Change Update Strategies
208
12.10
The
Maximin
Update Strategy
209
12.11
Cross Entropy Updating of Bayesian Nets
211
12.12
Compatibility and Indirect Evidence
216
12.13The Maxent Update Strategy
217
References
219
Index
235
Bayesian
nets are widely used in artificial intelligence as a calculus for
causal reasoning, enabling machines to make predictions, perform diag¬
noses, take decisions and even to discover causal relationships. This book,
aimed at researchers and graduate students in computer science, mathe¬
matics and philosophy, brings together two important research topics:
how to automate reasoning in artificial intelligence, and the nature of
causality and probability in philosophy. |
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| id | DE-604.BV021527784 |
| illustrated | Illustrated |
| index_date | 2024-07-02T14:24:25Z |
| indexdate | 2024-07-09T20:37:52Z |
| institution | BVB |
| isbn | 019853079X |
| language | English |
| lccn | 2005298047 |
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| physical | IX, 239 S. graph. Darst. 24 cm |
| publishDate | 2005 |
| publishDateSearch | 2005 |
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| publisher | Oxford Univ. Press |
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| spelling | Williamson, Jon Verfasser aut Bayesian nets and causality philosophical and computational foundations Jon Williamson 1. publ. Oxford [u.a.] Oxford Univ. Press 2005 IX, 239 S. graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references (p. 219-234) and index Causalité (Physique) Kunstmatige intelligentie gtt Methode van Bayes gtt Statistique bayésienne Bayesian statistical decision theory Causality (Physics) Bayes-Netz (DE-588)4567228-3 gnd rswk-swf Bayes-Netz (DE-588)4567228-3 s b DE-604 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014744176&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014744176&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Williamson, Jon Bayesian nets and causality philosophical and computational foundations Causalité (Physique) Kunstmatige intelligentie gtt Methode van Bayes gtt Statistique bayésienne Bayesian statistical decision theory Causality (Physics) Bayes-Netz (DE-588)4567228-3 gnd |
| subject_GND | (DE-588)4567228-3 |
| title | Bayesian nets and causality philosophical and computational foundations |
| title_auth | Bayesian nets and causality philosophical and computational foundations |
| title_exact_search | Bayesian nets and causality philosophical and computational foundations |
| title_exact_search_txtP | Bayesian nets and causality philosophical and computational foundations |
| title_full | Bayesian nets and causality philosophical and computational foundations Jon Williamson |
| title_fullStr | Bayesian nets and causality philosophical and computational foundations Jon Williamson |
| title_full_unstemmed | Bayesian nets and causality philosophical and computational foundations Jon Williamson |
| title_short | Bayesian nets and causality |
| title_sort | bayesian nets and causality philosophical and computational foundations |
| title_sub | philosophical and computational foundations |
| topic | Causalité (Physique) Kunstmatige intelligentie gtt Methode van Bayes gtt Statistique bayésienne Bayesian statistical decision theory Causality (Physics) Bayes-Netz (DE-588)4567228-3 gnd |
| topic_facet | Causalité (Physique) Kunstmatige intelligentie Methode van Bayes Statistique bayésienne Bayesian statistical decision theory Causality (Physics) Bayes-Netz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014744176&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014744176&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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