Computer scientists and statisticians introduce graduate students,researchers, and consultants to imprecise probability, a new framework for quantifying uncertainty and making inferences anddecisions under it. The editors established a common structure and a concise framework, including unique notation, then asked leadingresearchers to contribute chapters on their specialty. The book is therefore more cohesive than a simple collection of papers and morediverse than a single-author monograph. The topics include desirability, structural judgments, game-theory probability,statistical inference, classification, stochastic processes, reliability and risk, and computation.Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)
Computer scientists and statisticians introduce graduate students,researchers, and consultants to imprecise probability, a new framework for quantifying uncertainty and making inferences anddecisions under it. The editors established a common structure and a concise framework, including unique notation, then asked leadingresearchers to contribute chapters on their specialty. The book is therefore more cohesive than a simple collection of papers and morediverse than a single-author monograph. The topics include desirability, structural judgments, game-theory probability,statistical inference, classification, stochastic processes, reliability and risk, and computation.Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)
Computer scientists and statisticians introduce graduate students,researchers, and consultants to imprecise probability, a new framework for quantifying uncertainty and making inferences anddecisions under it. The editors established a common structure and a concise framework, including unique notation, then asked leadingresearchers to contribute chapters on their specialty. The book is therefore more cohesive than a simple collection of papers and morediverse than a single-author monograph. The topics include desirability, structural judgments, game-theory probability,statistical inference, classification, stochastic processes, reliability and risk, and computation.Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)
Computer scientists and statisticians introduce graduate students,researchers, and consultants to imprecise probability, a new framework for quantifying uncertainty and making inferences anddecisions under it. The editors established a common structure and a concise framework, including unique notation, then asked leadingresearchers to contribute chapters on their specialty. The book is therefore more cohesive than a simple collection of papers and morediverse than a single-author monograph. The topics include desirability, structural judgments, game-theory probability,statistical inference, classification, stochastic processes, reliability and risk, and computation.Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)
Preface
Introduction
Acknowledgements
Outline of this Book and Guide to Readers
Contributors
1 Desirability
1.1 Introduction
1.2 Reasoning about and with Sets of Desirable Gambles
1.2.1 Rationality Criteria
1.2.2 Assessments Avoiding Partial or Sure Loss
1.2.3 Coherent Sets of Desirable Gambles
1.2.4 Natural Extension
1.2.5 Desirability Relative to Subspaces with Arbitrary Vector Orderings
1.3 Deriving & Combining Sets of Desirable Gambles
1.3.1 Gamble Space Transformations
1.3.2 Derived Coherent Sets of Desirable Gambles
1.3.3 Conditional Sets of Desirable Gambles
1.3.4 Marginal Sets of Desirable Gambles
1.3.5 Combining Sets of Desirable Gambles
1.4 Partial Preference Orders
1.4.1 Strict Preference
1.4.2 Nonstrict Preference
1.4.3 Nonstrict Preferences Implied by Strict Ones
1.4.4 Strict Preferences Implied by Nonstrict Ones
1.5 Maximally Committal Sets of Strictly Desirable Gambles
1.6 Relationships with Other, Nonequivalent Models
1.6.1 Linear Previsions
1.6.2 Credal Sets
1.6.3 To Lower and Upper Previsions
1.6.4 Simplified Variants of Desirability
1.6.5 From Lower Previsions
1.6.6 Conditional Lower Previsions
1.7 Further Reading
2 Lower Previsions
2.1 Introduction
2.2 Coherent Lower Previsions
2.2.1 Avoiding Sure Loss and Coherence
2.2.2 Linear Previsions
2.2.3 Sets of Desirable Gambles
2.2.4 Natural Extension
2.3 Conditional Lower Previsions
2.3.1 Coherence of a Finite Number of Conditional Lower Previsions
2.3.2 Natural Extension of Conditional Lower Previsions
2.3.3 Coherence of an Unconditional and a Conditional Lower Prevision
2.3.4 Updating with the Regular Extension
2.4 Further Reading
2.4.1 The Work of Williams
2.4.2 The Work of Kuznetsov
2.4.3 The Work of Weichselberger
3 Structural Judgements
3.1 Introduction
3.2 Irrelevance and Independence
3.2.1 Epistemic Irrelevance
3.2.2 Epistemic Independence
3.2.3 Envelopes of Independent Precise Models
3.2.4 Strong Independence
3.2.5 The Formalist Approach to Independence
3.3 Invariance
3.3.1 Weak Invariance
3.3.2 Strong Invariance
3.4 Exchangeability.
3.4.1 Representation Theorem for Finite Sequences
3.4.2 Exchangeable Natural Extension
3.4.3 Exchangeable Sequences
3.5 Further Reading
3.5.1 Independence.
3.5.2 Invariance
3.5.3 Exchangeability
4 Special Cases
4.1 Introduction
4.2 Capacities and n-monotonicity
4.3 2-monotone Capacities
4.4 Probability Intervals on Singletons
4.5 1-monotone Capacities
4.5.1 Constructing 1-monotone Capacities
4.5.2 Simple Support Functions
4.5.3 Further Elements
4.6 Possibility Distributions, p-boxes, Clouds and Related Models.
4.6.1 Possibility Distributions
4.6.2 Fuzzy Intervals
4.6.3 Clouds
4.6.4 p-boxes.
4.7 Neighbourhood Models
4.7.1 Pari-mutuel
4.7.2 Odds-ratio
4.7.3 Linear-vacuous
4.7.4 Relations between Neighbourhood Models
4.8 Summary
5 Other Uncertainty Theories Based on Capacities
5.1 Imprecise Probability = Modal Logic + Probability
5.1.1 Boolean Possibility Theory and Modal Logic
5.1.2 A Unifying Framework for Capacity Based Uncertainty Theories
5.2 From Imprecise Probabilities to Belief Functions and Possibility Theory
5.2.1 Random Disjunctive Sets
5.2.2 Numerical Possibility Theory
5.2.3 Overall Picture
5.3 Discrepancies between Uncertainty Theories
5.3.1 Objectivist vs. Subjectivist Standpoints
5.3.2 Discrepancies in Conditioning
5.3.3 Discrepancies in Notions of Independence
5.3.4 Discrepancies in Fusion Operations
5.4 Further Reading
6 Game-Theoretic Probability
6.1 Introduction
6.2 A Law of Large Numbers
6.3 A General Forecasting Protocol
6.4 The Axiom of Continuity
6.5 Doob’s Argument
6.6 Limit Theorems of Probability
6.7 Lévy’s Zero-One Law.
6.8 The Axiom of Continuity Revisited
6.9 Further Reading
7 Statistical Inference
7.1 Background and Introduction
7.1.1 What is Statistical Inference?
7.1.2 (Parametric) Statistical Models and i.i.d. Samples
7.1.3 Basic Tasks and Procedures of Statistical Inference
7.1.4 Some Methodological Distinctions
7.1.5 Examples: Multinomial and Normal Distribution
7.2 Imprecision in Statistics, some General Sources and Motives
7.2.1 Model and Data Imprecision; Sensitivity Analysis and Ontological Views on Imprecision
7.2.2 The Robustness Shock, Sensitivity Analysis
7.2.3 Imprecision as a Modelling Tool to Express the Quality of Partial Knowledge
7.2.4 The Law of Decreasing Credibility
7.2.5 Imprecise Sampling Models: Typical Models and Motives
7.3 Some Basic Concepts of Statistical Models Relying on Imprecise Probabilities
7.3.1 Most Common Classes of Models and Notation
7.3.2 Imprecise Parametric Statistical Models and Corresponding i.i.d. Samples.
7.4 Generalized Bayesian Inference
7.4.1 Some Selected Results from Traditional Bayesian Statistics.
7.4.2 Sets of Precise Prior Distributions, Robust Bayesian Inference and the Generalized Bayes Rule
7.4.3 A Closer Exemplary Look at a Popular Class of Models: The IDM and Other Models Based on Sets of Conjugate Priors in Exponential Families.
7.4.4 Some Further Comments and a Brief Look at Other Models for Generalized Bayesian Inference
7.5 Frequentist Statistics with Imprecise Probabilities
7.5.1 The Non-robustness of Classical Frequentist Methods.
7.5.2 (Frequentist) Hypothesis Testing under Imprecise Probability: Huber-Strassen Theory and Extensions
7.5.3 Towards a Frequentist Estimation Theory under Imprecise Probabilities— Some Basic Criteria and First Results
7.5.4 A Brief Outlook on Frequentist Methods
7.6 Nonparametric Predictive Inference (NPI)
7.6.1 Overview
7.6.2 Applications and Challenges
7.7 A Brief Sketch of Some Further Approaches and Aspects
7.8 Data Imprecision, Partial Identification
7.8.1 Data Imprecision
7.8.2 Cautious Data Completion
7.8.3 Partial Identification and Observationally Equivalent Models
7.8.4 A Brief Outlook on Some Further Aspects
7.9 Some General Further Reading
7.10 Some General Challenges
8 Decision Making
8.1 Non-Sequential Decision Problems
8.1.1 Choosing From a Set of Gambles
8.1.2 Choice Functions for Coherent Lower Previsions
8.2 Sequential Decision Problems
8.2.1 Static Sequential Solutions: Normal Form
8.2.2 Dynamic Sequential Solutions: Extensive Form
8.3 Examples and Applications
8.3.1 Ellsberg’s Paradox
8.3.2 Robust Bayesian Statistics
9 Probabilistic Graphical Models
9.1 Introduction
9.2 Credal Sets
9.2.1 Definition and Relation with Lower Previsions
9.2.2 Marginalisation and Conditioning
9.2.3 Composition.
9.3 Independence
9.4 Credal Networks
9.4.1 Non-Separately Specified Credal Networks
9.5 Computing with Credal Networks
9.5.1 Credal Networks Updating
9.5.2 Modelling and Updating with Missing Data
9.5.3 Algorithms for Credal Networks Updating
9.5.4 Inference on Credal Networks as a Multilinear Programming Task
9.6 Further Reading
10 Classification
10.1 Introduction
10.2 Naive Bayes
10.3 Naive Credal Classifier (NCC)
10.4 Extensions and Developments of the Naive Credal Classifier
10.4.1 Lazy Naive Credal Classifier
10.4.2 Credal Model Averaging
10.4.3 Profile-likelihood Classifiers
10.4.4 Tree-Augmented Networks (TAN)
10.5 Tree-based Credal Classifiers
10.5.1 Uncertainty Measures on Credal Sets. The Maximum Entropy Function.
10.5.2 Obtaining Conditional Probability Intervals with the Imprecise Dirichlet Model
10.5.3 Classification Procedure
10.6 Metrics, Experiments and Software
10.6.1 Software.
10.6.2 Experiments.
11 Stochastic Processes
11.1 The Classical Characterization of Stochastic Processes
11.1.1 Basic Definitions
11.1.2 Precise Markov Chains
11.2 Event-driven Random Processes
11.3 Imprecise Markov Chains
11.3.1 From Precise to Imprecise Markov Chains
11.3.2 Imprecise Markov Models under Epistemic Irrelevance.
11.3.3 Imprecise Markov Models Under Strong Independence.
11.3.4 When Does the Interpretation of Independence (not) Matter?
11.4 Limit Behaviour of Imprecise Markov Chains
11.4.1 Metric Properties of Imprecise Probability Models
11.4.2 The Perron-Frobenius Theorem
11.4.3 Invariant Distributions
11.4.4 Coefficients of Ergodicity
11.4.5 Coefficients of Ergodicity for Imprecise Markov Chains.
11.5 Further Reading
12 Financial Risk Measurement
12.1 Introduction
12.2 Imprecise Previsions and Betting
12.3 Imprecise Previsions and Risk Measurement
12.3.1 Risk Measures as Imprecise Previsions
12.3.2 Coherent Risk Measures
12.3.3 Convex Risk Measures (and Previsions)
12.4 Further Reading
13 Engineering
13.1 Introduction
13.2 Probabilistic Dimensioning in a Simple Example
13.3 Random Set Modelling of the Output Variability
13.4 Sensitivity Analysis
13.5 Hybrid Models.
13.6 Reliability Analysis and Decision Making in Engineering
13.7 Further Reading
14 Reliability and Risk
14.1 Introduction
14.2 Stress-strength Reliability
14.3 Statistical Inference in Reliability and Risk
14.4 NPI in Reliablity and Risk
14.5 Discussion and Research Challenges
15 Elicitation
15.1 Methods and Issues
15.2 Evaluating Imprecise Probability Judgements
15.3 Factors Affecting Elicitation
15.4 Further Reading
16 Computation
16.1 Introduction
16.2 Natural Extension
16.2.1 Conditional Lower Previsions with Arbitrary Domains.
16.2.2 The Walley-Pelessoni-Vicig Algorithm
16.2.3 Choquet Integration
16.2.4 Möbius Inverse
16.2.5 Linear-Vacuous Mixture
16.3 Decision Making
16.3.1 Maximin, Maximax, and Hurwicz
16.3.2 Maximality
16.3.3 E-Admissibility
16.3.4 Interval Dominance
References
Author index
Subject index