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1.

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1. Actual causality / ((ebook) :)
Joseph Y. Halpern
出版情報:   1 online resource
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Preface
Introduction and Overview / 1：
Notes
The HP Definition of Causality / 2：
Causal Models / 2.1：
A Formal Definition of Actual Cause / 2.2：
A language for describing causality / 2.2.1：
The HP definition of actual causality / 2.2.2：
Examples / 2.3：
Transitivity / 2.4：
Probability and Causality / 2.5：
Sufficient Causality / 2.6：
Causality in Nonrecursive Models / 2.7：
AC2(bo)vs. AC2(bu) / 2.8：
Causal Paths / 2.9：
Proofs / 2.10：
Proof of Theorem 2.2.3 / 2.10.1：
Proof of Proposition 2.4.6 / 2.10.2：
Proof of Proposition 2.9.2 / 2.10.3：
Graded Causation and Normality / 3：
Defaults, Typicality, and Normality / 3.1：
Extended Causal Models / 3.2：
Graded Causation / 3.3：
More Examples / 3.4：
Knobe effects / 3.4.1：
Bogus prevention / 3.4.2：
Voting examples / 3.4.3：
Causal chains / 3.4.4：
Legal doctrines of intervening causes / 3.4.5：
An Alternative Approach to Incorporating Normality / 3.5：
The Art of Causal Modeling / 4：
Adding Variables to Structure a Causal Scenario / 4.1：
Conservative Extensions / 4.2：
Using the Original HP Definition Instead of the Updated Definition / 4.3：
The Stability of (Non-)Causality / 4.4：
The Range of Variables / 4.5：
Dependence and Independence / 4.6：
Dealing With Normality and Typicality / 4.7：
Proof of Lemma 4.2.2 / 4.8：
Proof of Theorem 4.3.1 / 4.8.2：
Proofs and example for Section 4.4 / 4.8.3：
Complexity and Axiomatization / 5：
Compact Representations of Structural Equations / 5.1：
Compact Representations of the Normality Ordering / 5.2：
Algebraic plausibility measures: the big picture / 5.2.1：
Piggy-backing on the causal model / 5.2.2：
The Complexity of Determining Causality / 5.3：
Axiomatizing Causal Reasoning / 5.4：
Technical Details and Proofs / 5.5：
Algebraic plausibility measures: the details / 5.5.1：
Proof of Theorems 5.3.1(c) and 5.3.2(b) / 5.5.2：
Proof of Theorems 5.4.1, 5.4.2, and 5.4.4 / 5.5.3：
Responsibility and Blame / 6：
A Naive Definition of Responsibility / 6.1：
Blame / 6.2：
Responsibility Normality, and Blame / 6.3：
Explanation / 7：
Explanation: The Basic Definition / 7.1：
Partial Explanations and Explanatory Power / 7.2：
The General Definition of Explanation / 7.3：
Applying the Definitions / 8：
Accountability / 8.1：
Causality in Databases / 8.2：
Program Verification / 8.3：
Last Words / 8.4：
References
Index
Preface
Introduction and Overview / 1：
Notes
2.

電子ブック
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2. High-dimensional probability : : an introduction with applications in data science ((ebook))
Roman Vershynin
出版情報:   1 online resource (xiv, 284 p.)
シリーズ名: Cambridge series in statistical and probabilistic mathematics ; 47
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3.

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3. Bayesian probability theory : : applications in the physical sciences ((ebook))
Wolfgang von der Linden, Graz University of Technology, Institute for Theoretical and Computational Physics, Graz, Austria, Volker Dose, Max Planck Institute for Plasma Physics, Garching, Germany, Udo von Toussaint, Max Planck Institute for Plasma Physics, Garching, Germany
出版情報:   1 online resource (xiii, 637 p.)
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4.

電子ブック
ＥＢ
4. Probability : a very short introduction ((ebook) :)
John Haigh
出版情報: Oxford : Oxford University Press, 2012  1 online resource (128 p.)
シリーズ名: Very short introductions ; 310
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5.

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5. Understanding probability. 3rd ed ((ebook))
Henk Tijms
出版情報:   1 online resource (x, 562 p.)
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Preface
Introduction
Probability in Action / Part I：
Probability questions / 1：
The law of large numbers and simulation / 2：
Probabilities in everyday life / 3：
Rare events and lotteries / 4：
Probability and statistics / 5：
Chance trees and Bayes' rule / 6：
Essentials of Probability / Part II：
Foundations of probability theory / 7：
Conditional probability and Bayes / 8：
Basic rules for discrete random variables / 9：
Continuous random variables / 10：
Jointly distributed random variables / 11：
Multivariate normal distribution / 12：
Conditioning by random variables / 13：
Generating functions / 14：
Discrete-time Markov chains / 15：
Continuous-time Markov chains / 16：
Appendix
Counting methods and ex
Recommended reading
Answers to odd-numbered problems
Bibliography
Index
Preface
Introduction
Probability in Action / Part I：
6.

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6. Probability : theory and examples. 5th ed (: electronic bk)
Rick Durrett
出版情報:   1 online resource (xii, 419 p.)
シリーズ名: Cambridge series in statistical and probabilistic mathematics ; 49
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7.

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7. A course in probability theory. 3rd ed (: electronic bk)
Kai Lai Chung
出版情報: San Diego ; Tokyo : Academic Press, c2001  1 online resource (xvi, 419 p. ; 23 cm)
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目次情報: 続きを見る
Preface to the third edition
Preface to the second edition
Preface to the first edition
Distribution function / 1：
Monotone functions / 1.1：
Distribution functions / 1.2：
Absolutely continuous and singular distributions / 1.3：
Measure theory / 2：
Classes of sets / 2.1：
Probability measures and their distribution functions / 2.2：
Random variable. Expectation. Independence / 3：
General definitions / 3.1：
Properties of mathematical expectation / 3.2：
Independence / 3.3：
Convergence concepts / 4：
Various modes of convergence / 4.1：
Almost sure convergence; Borel-Cantelli lemma / 4.2：
Vague convergence / 4.3：
Continuation / 4.4：
Uniform integrability; convergence of moments / 4.5：
Law of large numbers. Random series / 5：
Simple limit theorems / 5.1：
Weak law of large numbers / 5.2：
Convergence of series / 5.3：
Strong law of large numbers / 5.4：
Applications / 5.5：
Bibliographical Note
Characteristic function / 6：
General properties; convolutions / 6.1：
Uniqueness and inversion / 6.2：
Convergence theorems / 6.3：
Simple applications / 6.4：
Representation theorems / 6.5：
Multidimensional case; Laplace transforms / 6.6：
Central limit theorem and its ramifications / 7：
Liapounov's theorem / 7.1：
Lindeberg-Feller theorem / 7.2：
Ramifications of the central limit theorem / 7.3：
Error estimation / 7.4：
Law of the iterated logarithm / 7.5：
Infinite divisibility / 7.6：
Random walk / 8：
Zero-or-one laws / 8.1：
Basic notions / 8.2：
Recurrence / 8.3：
Fine structure / 8.4：
Conditioning. Markov property. Martingale / 8.5：
Basic properties of conditional expectation / 9.1：
Conditional independence; Markov property / 9.2：
Basic properties of smartingales / 9.3：
Inequalities and convergence / 9.4：
Supplement: Measure and Integral / 9.5：
Construction of measure
Characterization of extensions
Measures in R
Integral
General Bibliography
Index
Preface to the third edition
Preface to the second edition
Preface to the first edition
8.

電子ブック
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8. Probabilistic machine learning : an introduction (: electronic bk)
Kevin P. Murphy
出版情報: [Ann Arbor, Mich.] : ProQuest Ebook Central, [202-]  1 online resource
シリーズ名: Adaptive computation and machine learning
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9.

電子ブック
ＥＢ
9. The Arrow impossibility theorem (: electronic bk)
Eric Maskin, Amartya Sen ; with Kenneth J. Arrow ... [et al.]
出版情報: [S.l.] : EBSCOhost, [20--]  1 online resource (vi, 152 p.)
シリーズ名: Kenneth J. Arrow lecture series ;
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10.

電子ブック
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10. Measure, integral, and probability (: electronic bk)
Marek Capiński and Ekkehard Kopp
出版情報: London : Springer, [20--]  1 online resource (xi, 227 p.)
シリーズ名: Springer undergraduate mathematics series
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Motivation and preliminaries / 1：
Notation and basic set theory / 1.1：
Sets and functions / 1.1.1：
Countable and uncountable sets in <$>\cal {R}<$> / 1.1.2：
Topological properties of sets in <$>\cal {R}<$> / 1.1.3：
The Riemann integral: scope and limitations / 1.2：
Choosing numbers at random / 1.3：
Measure / 2：
Null sets / 2.1：
Outer measure / 2.2：
Lebesgue measurable sets and Lebesgue measure / 2.3：
Basic properties of Lebesgue measure / 2.4：
Borel sets / 2.5：
Probability / 2.6：
Probability space / 2.6.1：
Events: conditioning and independence / 2.6.2：
Proofs of propositions / 2.7：
Measurable functions / 3：
The extended real line / 3.1：
Definition / 3.2：
Examples / 3.3：
Properties / 3.4：
Random variables / 3.5：
Sigma fields generated by random variables / 3.5.2：
Probability distributions / 3.5.3：
Independence of random variables / 3.5.4：
Integral / 3.6：
Definition of the integral / 4.1：
Monotone Convergence Theorems / 4.2：
Integrable functions / 4.3：
The Dominated Convergence Theorem / 4.4：
Relation to the Riemann integral / 4.5：
Approximation of measurable functions / 4.6：
Integration with respect to probability distributions / 4.7：
Absolutely continuous measures: examples of densities / 4.7.2：
Expectation of a random variable / 4.7.3：
Characteristic function / 4.7.4：
Spaces of integrable functions / 4.8：
The space L1 / 5.1：
The Hilbert space L2 / 5.2：
Properties of the L2-norm / 5.2.1：
Inner product spaces / 5.2.2：
Orthogonality / 5.2.3：
The Lp spaces: completeness / 5.3：
Moments / 5.4：
Independence / 5.4.2：
Product measures / 5.5：
Multi-dimensional Lebesgue measure / 6.1：
Product σ-fields / 6.2：
Construction of the product measure / 6.3：
Fubini's Theorem / 6.4：
Joint distributions / 6.5：
Independence again / 6.5.2：
Conditional probability / 6.5.3：
Characteristic functions determine distributions / 6.5.4：
Limit theorems / 6.6：
Modes of convergence / 7.1：
Convergence in probability / 7.2：
Weak law of large numbers / 7.2.2：
Borel-Cantelli lemmas / 7.2.3：
Strong law of large numbers / 7.2.4：
Weak convergence / 7.2.5：
Central Limit Theorem / 7.2.6：
Solutions to exercises / 7.3：
Appendix / 9：
References
Index
Motivation and preliminaries / 1：
Notation and basic set theory / 1.1：
Sets and functions / 1.1.1：
11.

電子ブック
ＥＢ
11. Introduction to empirical processes and semiparametric inference (: electronic bk)
Michael R. Kosorok
出版情報: [Berlin] : SpringerLink, [20--]  1 online resource (xiv, 483 p.)
シリーズ名: Springer series in statistics
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Preface
Overview / I：
Introduction / 1：
An Overview of Empirical Processes / 2：
The Main Features / 2.1：
Empirical Process Techniques / 2.2：
Stochastic Convergence / 2.2.1：
Entropy for Glivenko-Cantelli and Donsker Theorems / 2.2.2：
Bootstrapping Empirical Processes / 2.2.3：
The Functional Delta Method / 2.2.4：
Z-Estimators / 2.2.5：
M-Estimators / 2.2.6：
Other Topics / 2.3：
Exercises / 2.4：
Notes / 2.5：
Overview of Semiparametric Inference / 3：
Semiparametric Models and Efficiency / 3.1：
Score Functions and Estimating Equations / 3.2：
Maximum Likelihood Estimation / 3.3：
Case Studies I / 3.4：
Linear Regression / 4.1：
Mean Zero Residuals / 4.1.1：
Median Zero Residuals / 4.1.2：
Counting Process Regression / 4.2：
The General Case / 4.2.1：
The Cox Model / 4.2.2：
The Kaplan-Meier Estimator / 4.3：
Efficient Estimating Equations for Regression / 4.4：
Simple Linear Regression / 4.4.1：
A Poisson Mixture Regression Model / 4.4.2：
Partly Linear Logistic Regression / 4.5：
Empirical Processes / 4.6：
Introduction to Empirical Processes / 5：
Preliminaries for Empirical Processes / 6：
Metric Spaces / 6.1：
Outer Expectation / 6.2：
Linear Operators and Functional Differentiation / 6.3：
Proofs / 6.4：
Stochastic Processes in Metric Spaces / 6.5：
Weak Convergence / 7.2：
General Theory / 7.2.1：
Spaces of Bounded Functions / 7.2.2：
Other Modes of Convergence / 7.3：
Empirical Process Methods / 7.4：
Maximal Inequalities / 8.1：
Orlicz Norms and Maxima / 8.1.1：
Maximal Inequalities for Processes / 8.1.2：
The Symmetrization Inequality and Measurability / 8.2：
Glivenko-Cantelli Results / 8.3：
Donsker Results / 8.4：
Entropy Calculations / 8.5：
Uniform Entropy / 9.1：
VC-Classes / 9.1.1：
BUEI Classes / 9.1.2：
Bracketing Entropy / 9.2：
Glivenko-Cantelli Preservation / 9.3：
Donsker Preservation / 9.4：
The Bootstrap for Donsker Classes / 9.5：
An Unconditional Multiplier Central Limit Theorem / 10.1.1：
Conditional Multiplier Central Limit Theorems / 10.1.2：
Bootstrap Central Limit Theorems / 10.1.3：
Continuous Mapping Results / 10.1.4：
The Bootstrap for Glivenko-Cantelli Classes / 10.2：
A Simple Z-Estimator Master Theorem / 10.3：
Additional Empirical Process Results / 10.4：
Bounding Moments and Tail Probabilities / 11.1：
Sequences of Functions / 11.2：
Contiguous Alternatives / 11.3：
Sums of Independent but not Identically Distributed Stochastic Processes / 11.4：
Central Limit Theorems / 11.4.1：
Bootstrap Results / 11.4.2：
Function Classes Changing with n / 11.5：
Dependent Observations / 11.6：
Main Results and Proofs / 11.7：
Examples / 12.2：
Composition / 12.2.1：
Integration / 12.2.2：
Product Integration / 12.2.3：
Inversion / 12.2.4：
Other Mappings / 12.2.5：
Consistency / 12.3：
The General Setting / 13.2：
Using Donsker Classes / 13.2.2：
A Master Theorem and the Bootstrap / 13.2.3：
Using the Delta Method / 13.3：
The Argmax Theorem / 13.4：
Rate of Convergence / 14.2：
Regular Euclidean M-Estimators / 14.4：
Non-Regular Examples / 14.5：
A Change-Point Model / 14.5.1：
Monotone Density Estimation / 14.5.2：
Case Studies II / 14.6：
Partly Linear Logistic Regression Revisited / 15.1：
The Two-Parameter Cox Score Process / 15.2：
The Proportional Odds Model Under Right Censoring / 15.3：
Nonparametric Maximum Likelihood Estimation / 15.3.1：
Existence / 15.3.2：
Score and Information Operators / 15.3.3：
Weak Convergence and Bootstrap Validity / 15.3.5：
Testing for a Change-point / 15.4：
Large p Small n Asymptotics for Microarrays / 15.5：
Assessing P-Value Approximations / 15.5.1：
Consistency of Marginal Empirical Distribution Functions / 15.5.2：
Inference for Marginal Sample Means / 15.5.3：
Semiparametric Inference / 15.6：
Introduction to Semiparametric Inference / 16：
Preliminaries for Semiparametric Inference / 17：
Projections / 17.1：
Hilbert Spaces / 17.2：
More on Banach Spaces / 17.3：
Tangent Sets and Regularity / 17.4：
Efficiency / 18.2：
Optimality of Tests / 18.3：
Efficient Inference for Finite-Dimensional Parameters / 18.4：
Efficient Score Equations / 19.1：
Profile Likelihood and Least-Favorable Submodels / 19.2：
The Cox Model for Right Censored Data / 19.2.1：
The Proportional Odds Model for Right Censored Data / 19.2.2：
The Cox Model for Current Status Data / 19.2.3：
Inference / 19.2.4：
Quadratic Expansion of the Profile Likelihood / 19.3.1：
The Profile Sampler / 19.3.2：
The Penalized Profile Sampler / 19.3.3：
Other Methods / 19.3.4：
Efficient Inference for Infinite-Dimensional Parameters / 19.4：
Semiparametric Maximum Likelihood Estimation / 20.1：
Weighted and Nonparametric Bootstraps / 20.2：
The Piggyback Bootstrap / 20.2.2：
Semiparametric M-Estimation / 20.2.3：
Semiparametric M-estimators / 21.1：
Motivating Examples / 21.1.1：
General Scheme for Semiparametric M-Estimators / 21.1.2：
Consistency and Rate of Convergence / 21.1.3：
[radical]n Consistency and Asymptotic Normality / 21.1.4：
Weighted M-Estimators and the Weighted Bootstrap / 21.2：
Entropy Control / 21.3：
Examples Continued / 21.4：
Cox Model with Current Status Data (Example 1, Continued) / 21.4.1：
Binary Regression Under Misspecified Link Function (Example 2, Continued) / 21.4.2：
Mixture Models (Example 3, Continued) / 21.4.3：
Penalized M-estimation / 21.5：
Two Other Examples / 21.5.1：
Case Studies III / 21.6：
The Proportional Odds Model Under Right Censoring Revisited / 22.1：
Efficient Linear Regression / 22.2：
Temporal Process Regression / 22.3：
A Partly Linear Model for Repeated Measures / 22.4：
References / 22.5：
Author Index
List of symbols
Subject Index
Preface
Overview / I：
Introduction / 1：
12.

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12. The theory of probability (: electronic bk)
Santosh S. Venkatesh
出版情報:   1 online resource (xxiv, 805 p.)
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Elements / Part I：
Probability spaces / 1：
Conditional probability / 2：
A first look at independence / 3：
Probability sieves / 4：
Numbers play a game of chance / 5：
The normal law / 6：
Probabilities on the real line / 7：
The Bernoulli schema / 8：
The essence of randomness / 9：
The coda of the normal / 10：
Foundations / Part II：
Distribution functions and measure / 11：
Random variables / 12：
Great expectations / 13：
Variations on a theme of integration / 14：
Laplace transforms / 15：
The law of large numbers / 16：
From inequalities to concentration / 17：
Poisson approximation / 18：
Convergence in law, selection theorems / 19：
Normal approximation / 20：
Appendices / Part III：
Sequences, functions, spaces / 21：
Elements / Part I：
Probability spaces / 1：
Conditional probability / 2：
13.

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13. Biological sequence analysis : : probabalistic models of proteins and nucleic acids (: electronic bk)
Richard Durbin [and three others]
出版情報:   1 online resource (xi, 356 p.)
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Introduction / 1：
Pairwise sequence alignment / 2：
Multiple alignments / 3：
Hidden Markov models / 4：
Hidden Markov models applied to biological sequences / 5：
The Chomsky hierarchy of formal grammars / 6：
RNA and stochastic context-free grammars / 7：
Phylogenetic trees / 8：
Phylogeny and alignment / 9：
Index
Preface
Tools for demography and epidemiology
Identification of population oscillations: a case study
Density-dependent control and feedback
Modelling the endogenous oscillations and predictions from time-series analysis
Cycles in the grain price series
Interactions of exogenous cycles: a case study
Mortality crises and the effects of the price of wool
Modelling epidemics for the demographer: the dynamics of smallpox in London
Non-linear modelling of the two-yearly epidemics in smallpox: the genesis of chaos? / 10：
Measles and whooping cough in London / 11：
Integration of the dynamics of infectious diseases with the demography of London / 12：
Smallpox in rural towns in England in the seventeenth and eighteenth centuries / 13：
Infectious diseases in England and Wales in the nineteenth century / 14：
Prospectives - towards a meta-population study / 15：
References
Introduction / 1：
Pairwise sequence alignment / 2：
Multiple alignments / 3：
14.

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14. Variational Bayesian learning theory (: electronic bk)
Shinichi Nakajima, Kazuho Watanabe, Masashi Sugiyama
出版情報:   1 online resource (xv, 543 p.)
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15.

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15. Probability theory : an analytic view. 2nd ed (: electronic bk)
Daniel W. Stroock
出版情報: Cambridge : Cambridge University Press, 2011  1 online resource (xxi, 527 p.)
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Sums of independent random variables / 1：
The central limit theorem / 2：
Infinitely divisible laws / 3：
Levy processes / 4：
Conditioning and martingales / 5：
Some extensions and applications of martingale theory / 6：
Continuous parameter martingales / 7：
Gaussian measures on a Banach space / 8：
Convergence of measures on a Polish space / 9：
Wiener measure and partial differential equations / 10：
Some classical potential theory / 11：
Sums of independent random variables / 1：
The central limit theorem / 2：
Infinitely divisible laws / 3：
16.

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16. Practice of Bayesian probability theory in geotechnical engineering
Wan-Huan Zhou, Zhen-Yu Yin, Ka-Veng Yuen
出版情報: EBSCOhost  1 online resource (xxvii, 324 p.)
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17.

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17. Bandit Algorithms ((ebook))
Tor Lattimore, Csaba Szepesvári
出版情報:   1 online resource (xviii, 518 p.)
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18.

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18. Prospect theory : for risk and ambiguity (: electronic bk)
Peter P. Wakker
出版情報:   1 online resource (xiii, 503 p.)
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Preface
Introduction
Expected Utility / Part I：
The general model of decision under uncertainty no-arbitrage (expected utility with known utilities and unknown probabilities) / 1：
Expected utility with known probabilities - 'risk' - and unknown utilities / 2：
Applications of expected utility for risk / 3：
Expected utility with unknown probabilities and unknown utilities / 4：
Nonexpected Utility for Risk / Part II：
Heuristic arguments for probabilistic sensitivity and rank dependence / 5：
Probabilistic sensitivity and rank dependence analyzed / 6：
Applications and extensions of rank dependence / 7：
Where prospect theory deviates from rank-dependent utility and expected utility: reference dependence versus asset integration / 8：
Prospect theory for decision under risk / 9：
Nonexpected Utility for Uncertainty / Part III：
Extending rank-dependent utility from risk to uncertainty / 10：
Ambiguity: where uncertainty extends beyond risk / 11：
Prospect theory for uncertainty / 12：
Conclusion / 13：
Appendices
References
Index
Preface
Introduction
Expected Utility / Part I：
19.

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19. Causality : models, reasoning, and inference (: electronic bk)
Judea Pearl
出版情報:   1 online resource (xvi, 384 p.)
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Introduction to probabilities, graphs, and causal models / 1：
A theory of inferred causation / 2：
Causal diagrams and the identification of causal effects / 3：
Actions, plans, and direct effects / 4：
Causality and structural models in social science and economics / 5：
Simpson's paradox, confounding, and collapsibility / 6：
The logic of structure-based counterfactuals / 7：
Imperfect experiments: bounding effects and counterfactuals / 8：
Probability of causation: interpretation and identification / 9：
The actual cause / 10：
Introduction to probabilities, graphs, and causal models / 1：
A theory of inferred causation / 2：
Causal diagrams and the identification of causal effects / 3：
20.

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20. Lectures on the Poisson Process / ((ebook))
Günter Last, Mathew Penrose
出版情報:   1 online resource (xx, 293 pages)
シリーズ名: Institute of Mathematical Statistics textbooks ; 7
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21.

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21. Exercises in probability : : a guided tour from measure theory to random processes, via conditioning. Second edition ((ebook))
Loïc Chaumont, Marc Yor
出版情報:   1 online resource (xx, 279 pages)
シリーズ名: Cambridge series on statistical and probabilistic mathematics ; 35
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目次情報: 続きを見る
Preface to the Second Edition
Preface to the First Edition
Measure theory and probability / 1：
Independence and conditioning / 2：
Gaussian variables / 3：
Distributional computations / 4：
Convergence of random variables / 5：
Random processes / 6：
Where is the notion N discussed?
Final suggestions: how to go further?
References
Index
Preface to the Second Edition
Preface to the First Edition
Measure theory and probability / 1：
文献の複写および貸借の依頼を行う
 文献複写・貸借依頼