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1.

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1. 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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2.

電子ブック
ＥＢ
2. Stochastic processes for physicists : understanding noisy system (: electronic bk)
Kurt Jacobs
出版情報: Cambridge Core  1 online resource (xiii, 188 p.)
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目次情報: 続きを見る
A review of probability theory / 1：
Differential equations / 2：
Stochastic equations with Gaussian noise / 3：
Further properties of stochastic processes / 4：
Some applications of Gaussian noise / 5：
Numerical methods for Gaussian noise / 6：
Fokker-Planck equations and reaction-diffusion systems / 7：
Jump processes / 8：
Levy processes / 9：
Modern probability theory / 10：
Appendix
References
Index
A review of probability theory / 1：
Differential equations / 2：
Stochastic equations with Gaussian noise / 3：
3.

電子ブック
ＥＢ
3. Stochastic Dynamics of Structures. (: electronic bk)
出版情報: [S.l.] : Wiley-ISTE.  1 online resource
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4.

電子ブック
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4. Hilbert and Banach space-valued stochastic processes (: electronic bk)
Yûichirô Kakihara
出版情報: World Scientific eBooks  1 online resource (xi, 526 p.)
シリーズ名: Series on multivariate analysis ; v. 13
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5.

電子ブック
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5. Stochastic processes : harmonizable theory (: electronic bk)
M M Rao
出版情報: World Scientific eBooks  1 online resource (xii, 328 p.)
シリーズ名: Series on multivariate analysis ; v. 12
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6.

電子ブック
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6. Generalized functionals of Brownian motion and their applications : nonlinear functionals of fundamental stochastic processes (: electronic bk)
N.U. Ahmed
出版情報: [Singapore] : World Scientific, [20--]  1 online resource (xiv, 299 p.)
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目次情報: 続きを見る
Preface
Background Material / 1：
Introduction / 1.1：
Wiener Process and Wiener Measure / 1.2：
Stochastic Differential Equations in Rd / 1.3：
Stochastic Differential Equations in H / 1.4：
Measure Solutions / 1.4.1：
Nonlinear Filtering / 1.5：
Finite Dimensional Filtering / 1.5.1：
Infinite Dimensional Filtering / 1.5.2：
Elements of Vector Measures / 1.6：
Some Problems for Exercise / 1.7：
Regular Functionals of Brownian Motion / 2：
Functionals of Scalar Brownian Motion / 2.1：
Functionals of Vector Brownian Motion / 2.3：
Functionals of Gaussian Random Field (GRF) / 2.4：
Functionals of Multidimensional Gaussian Random Fields / 2.5：
Functionals of co-Dimensional Brownian Motion / 2.6：
Fr-Br. Motion and Regular Functionals Thereof / 2.7：
Levy Process and Regular Functionals Thereof / 2.8：
Generalized Functionals of the First Kind I / 2.9：
Mild Generalized Functionals I / 3.1：
Mild Generalized Functionals II / 3.3：
Generalized Functionals of GRF I / 3.4：
Generalized Functionals of GRF II / 3.5：
Generalized Functionals of co-Dim. Brownian Motion / 3.6：
Generalized Functionals of Fr.Brownian Motion and Levy Process / 3.7：
Functional Analysis on {G, g} and Their Duals / 3.8：
Compact and Weakly Compact Sets / 4.1：
Some Optimization Problems / 4.3：
Applications to SDE / 4.4：
Vector Measures / 4.5：
Application to Nonlinear Filtering / 4.6：
Application to Infinite Dimensional Systems / 4.7：
Levy Optimization Problem / 4.8：
I2-Based Generalized Functionals of White Noise II / 4.9：
Characteristic Function of White Noise / 5.1：
Multiple Wiener-Ito Integrals / 5.3：
Generalized Hida-Functionals / 5.4：
Application to Quantum Mechanics / 5.5：
Lp-Based Generalized Functionals of White Noise III / 5.6：
Homogeneous Functionals of Degree n / 6.1：
Nonhomogeneous Functionals / 6.3：
Weighted Generalized Functionals / 6.4：
Some Examples Related to Section 6.4 / 6.5：
Generalized Functionals of Random Fields Applied / 6.6：
Fq-Valued Vector Measures with Application / 6.7：
Wpm Based Generalized Functionals of White Noise IV / 6.8：
Homogeneous Functionals / 7.1：
Inductive and Projective Limits / 7.3：
Abstract Generalized Functionals / 7.5：
Vector Measures with Values from Wiener-Ito Distributions / 7.6：
Application to Stochastic Navier-Stokes Equation / 7.7：
Some Elements of Malliavin Calculus / 7.9：
Abstract Wiener Space / 8.1：
Malliavin Derivative and Integration by Parts / 8.3：
Operator d the Adjoint of the Operator D / 8.4：
Ornstein-Uhlenbeck Operator L / 8.5：
Sobolev Spaces on Wiener Measure Space fi = (fi,/i) / 8.6：
Smoothness of Probability Measures / 8.7：
Smoothness under Ellipticity Condition / 8.7.1：
Smoothness under Horrnander's Conditions / 8.7.2：
Some Illustrative Examples / 8.7.3：
Some Comments on Degeneracy Condition / 8.7.4：
Central Limit Theorem for Wiener-Ito Functionals / 8.8：
Malliavin Calculus for Fr-Brownian Motion / 8.9：
Evolution Equations on Fock Spaces / 8.10：
Malliavin Operators on Fock Spaces / 9.1：
Evolution Equations on Abstract Fock Spaces / 9.3：
Evolution Equations Determined by Coercive Operators on Fock Spaces / 9.4：
An Example / 9.5：
Evolution Equations on Wiener-Sobolev Spaces / 9.6：
Some Examples for Exercise / 9.7：
Bibliography
Index
Preface
Background Material / 1：
Introduction / 1.1：
7.

電子ブック
ＥＢ
7. Random data : analysis and measurement procedures. 4th ed (: electronic bk)
Julius S. Bendat, Allan G. Piersol
出版情報: [Hoboken, N.J.] : Wiley Online Library, 2012, c2010  1 online resource (xxi, 613 p.)
シリーズ名: Wiley series in probability and mathematical statistics ;
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8.

電子ブック
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8. Stochastic calculus of variations in mathematical finance
Paul Malliavin, Anton Thalmaier
出版情報: Berlin : Springer, [20--]  1 online resource (xi, 142 p.)
シリーズ名: Springer finance
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Gaussian Stochastic Calculus of Variations / 1：
Finite-Dimensional Gaussian Spaces, Hermite Expansion / 1.1：
Wiener Space as Limit of its Dyadic Filtration / 1.2：
Stroock-Sobolev Spaces of Functionals on Wiener Space / 1.3：
Divergence of Vector Fields, Integration by Parts / 1.4：
Ito's Theory of Stochastic Integrals / 1.5：
Differential and Integral Calculus in Chaos Expansion / 1.6：
Monte-Carlo Computation of Divergence / 1.7：
Computation of Greeks and Integration by Parts Formulae / 2：
PDE Option Pricing; PDEs Governing the Evolution of Greeks / 2.1：
Stochastic Flow of Diffeomorphisms; Ocone-Karatzas Hedging / 2.2：
Principle of Equivalence of Instantaneous Derivatives / 2.3：
Pathwise Smearing for European Options / 2.4：
Examples of Computing Pathwise Weights / 2.5：
Pathwise Smearing for Barrier Option / 2.6：
Market Equilibrium and Price-Volatility Feedback Rate / 3：
Natural Metric Associated to Pathwise Smearing / 3.1：
Price-Volatility Feedback Rate / 3.2：
Measurement of the Price-Volatility Feedback Rate / 3.3：
Market Ergodicity and Price-Volatility Feedback Rate / 3.4：
Multivariate Conditioning and Regularity of Law / 4：
Non-Degenerate Maps / 4.1：
Divergences / 4.2：
Regularity of the Law of a Non-Degenerate Map / 4.3：
Multivariate Conditioning / 4.4：
Riesz Transform and Multivariate Conditioning / 4.5：
Example of the Univariate Conditioning / 4.6：
Non-Elliptic Markets and Instability in HJM Models / 5：
Notation for Diffusions on R[superscript N] / 5.1：
The Malliavin Covariance Matrix of a Hypoelliptic Diffusion / 5.2：
Malliavin Covariance Matrix and Hormander Bracket Conditions / 5.3：
Regularity by Predictable Smearing / 5.4：
Forward Regularity by an Infinite-Dimensional Heat Equation / 5.5：
Instability of Hedging Digital Options in HJM Models / 5.6：
Econometric Observation of an Interest Rate Market / 5.7：
Insider Trading / 6：
A Toy Model: the Brownian Bridge / 6.1：
Information Drift and Stochastic Calculus of Variations / 6.2：
Integral Representation of Measure-Valued Martingales / 6.3：
Insider Additional Utility / 6.4：
An Example of an Insider Getting Free Lunches / 6.5：
Asymptotic Expansion and Weak Convergence / 7：
Asymptotic Expansion of SDEs Depending on a Parameter / 7.1：
Watanabe Distributions and Descent Principle / 7.2：
Strong Functional Convergence of the Euler Scheme / 7.3：
Weak Convergence of the Euler Scheme / 7.4：
Stochastic Calculus of Variations for Markets with Jumps / 8：
Probability Spaces of Finite Type Jump Processes / 8.1：
Stochastic Calculus of Variations for Exponential Variables / 8.2：
Stochastic Calculus of Variations for Poisson Processes / 8.3：
Mean-Variance Minimal Hedging and Clark-Ocone Formula / 8.4：
Volatility Estimation by Fourier Expansion / A：
Fourier Transform of the Volatility Functor / A.1：
Numerical Implementation of the Method / A.2：
Strong Monte-Carlo Approximation of an Elliptic Market / B：
Definition of the Scheme [characters not reproducible] / B.1：
The Milstein Scheme / B.2：
Horizontal Parametrization / B.3：
Reconstruction of the Scheme [characters not reproducible] / B.4：
Numerical Implementation of the Price-Volatility Feedback Rate / C：
References
Index
Gaussian Stochastic Calculus of Variations / 1：
Finite-Dimensional Gaussian Spaces, Hermite Expansion / 1.1：
Wiener Space as Limit of its Dyadic Filtration / 1.2：
9.

電子ブック
ＥＢ
9. 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：
10.

電子ブック
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10. Online stochastic combinatorial optimization (: electronic bk)
Pascal Van Hentenryck and Russell Bent
出版情報: EBSCOhost  1 online resource (xiii, 232 p.)
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Preface
Introduction / 1：
From A Priori to Online Stochastic Optimization / 1.1：
Online Stochastic Combinatorial Optimization / 1.2：
Online Anticipatory Algorithms / 1.3：
Online Stochastic Combinatorial Optimization in Context / 1.4：
Organization and Themes / 1.5：
Online Stochastic Scheduling / I：
The Generic Offline Problem / 2：
The Online Problem / 2.2：
The Generic Online Algorithm / 2.3：
Properties of Online Stochastic Scheduling / 2.4：
Oblivious Algorithms / 2.5：
The Expectation Algorithm / 2.6：
The Consensus Algorithm / 2.7：
The Regret Algorithm / 2.8：
Immediate Decision Making / 2.9：
The Suboptimality Approximation Problem / 2.10：
Notes and Further Reading / 2.11：
Theoretical Analysis / 3：
Expected Loss / 3.1：
Local Errors / 3.2：
Bounding Local Errors / 3.3：
The Theoretical Results / 3.4：
Discussion on the Theoretical Assumptions / 3.5：
Packet Scheduling / 3.6：
The Packet Scheduling Problem / 4.1：
The Optimization Algorithm / 4.2：
The Greedy Algorithm is Competitive / 4.3：
The Suboptimality Approximation / 4.4：
Experimental Setting / 4.5：
Experimental Results / 4.6：
The Anticipativity Assumption / 4.7：
Online Stochastic Reservations / 4.8：
The Offline Reservation Problem / 5：
Cancellations / 5.2：
Online Multiknapsack Problems / 6：
Online Multiknapsack with Deadlines / 6.1：
Online Stochastic Routing / 6.2：
Vehicle Routing with Time Windows / 7：
Vehicle Dispatching and Routing / 7.1：
Large Neighborhood Search / 7.2：
Online Stochastic Vehicle Routing / 7.3：
Online Single Vehicle Routing / 8.2：
Service Guarantees / 8.3：
A Waiting Strategy / 8.4：
A Relocation Strategy / 8.5：
Multiple Pointwise Decisions / 8.6：
Online Vehicle Dispatching / 8.7：
The Online Vehicle Dispatching Problems / 9.1：
Setting of the Algorithms / 9.2：
Visualizations of the Algorithms / 9.3：
Online Vehicle Routing with Time Windows / 9.5：
The Online Instances / 10.1：
Learning and Historical Sampling / 10.2：
Learning Distributions / 11：
The Learning Framework / 11.1：
Hidden Markov Models / 11.2：
Learning Hidden Markov Models / 11.3：
Historical Sampling / 11.4：
Historical Averaging / 12.1：
Sequential Decision Making / 12.2：
Markov Chance-Decision Processes / 13：
Motivation / 13.1：
Decision-Chance versus Chance-Decision / 13.2：
Equivalence of MDCPs and MCDPs / 13.3：
The Approximation Theorem for Anticipative MCDPs / 13.4：
Beyond Anticipativity / 13.6：
The General Approximation Theorem for MCDPs / 13.8：
References / 13.9：
Index
Preface
Introduction / 1：
From A Priori to Online Stochastic Optimization / 1.1：
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