Probability
著者
書誌事項
Probability
(Springer texts in statistics)
Springer-Verlag, c1993
- : us
- : gw
- : pbk
大学図書館所蔵 全49件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references (p. 277-278) and index
Publisher of pbk.: Springer Science+Business Media
内容説明・目次
- 巻冊次
-
: us ISBN 9780387940717
内容説明
This book offers a straightforward introduction to the mathematical theory of probability. It presents the central results and techniques of the subject in a complete and self-contained account. As a result, the emphasis is on giving results in simple forms with clear proofs and to eschew more powerful forms of theorems which require technically involved proofs. Throughout there are a wide variety of exercises to illustrate and to develop ideas in the text.
目次
Prelude: Random Walks.- The Model.- Random variables.- Probability.- First calculations.- Issues and Approaches.- Issues.- Approaches.- Tools.- Functional of the Random Walk.- Times of returns to the origin.- Numbers of returns to the origin.- First passage times.- Maxima.- Time spent positive.- Limit Theorems.- Summary.- 1 Probability.- 1.1 Random Experiments and Sample Spaces.- 1.1.1 Random experiments.- 1.1.2 Sample spaces.- 1.2 Events and Classes of Sets.- 1.2.1 Events.- 1.2.2 Basic set operations.- 1.2.3 Indicator functions.- 1.2.4 Operations on sequences of sets.- 1.2.5 Classes of sets closed under set operations.- 1.2.6 Generated classes.- 1.2.7 The monotone class theorem.- 1.2.8 Events, bis.- 1.3 Probabilities and Probability Spaces.- 1.3.1 Probability.- 1.3.2 Elementary properties.- 1.3.3 More advanced properties.- 1.3.4 Almost sure and null events.- 1.3.5 Uniqueness.- 1.4 Probabilities on R.- 1.4.1 Distribution functions.- 1.4.2 Discrete probabilities.- 1.4.3 Absolutely continuous probabilities.- 1.4.4 Mixed distributions.- 1.5 Conditional Probability Given a Set.- 1.6 Complements.- 1.6.1 The extended real numbers.- 1.6.2 Measures.- 1.6.3 Lebesgue measure.- 1.6.4 Singular probabilities on R.- 1.6.5 Representation of probabilities on R.- 1.7 Exercises.- 2 Random Variables.- 2.1 Fundamentals.- 2.1.1 Random variables.- 2.1.2 Random vectors.- 2.1.3 Stochastic processes.- 2.1.4 Complex-valued random variables.- 2.1.5 The -algebra generated by a random variable.- 2.1.6 Simplified criteria.- 2.2 Combining Random Variables.- 2.2.1 Algebraic operations.- 2.2.2 Limiting operations.- 2.2.3 Transformations.- 2.2.4 Approximation of positive random variables.- 2.2.5 Monotone class theorems.- 2.3 Distributions and Distribution Functions.- 2.3.1 Random variables.- 2.3.2 Random vectors.- 2.4 Key Random Variables and Distributions.- 2.4.1 Discrete random variables.- 2.4.2 Absolutely continuous random variables.- 2.4.3 Random vectors.- 2.5 Transformation Theory.- 2.5.1 Random variables.- 2.5.2 Random vectors.- 2.6 Random Variables with Prescribed Distributions.- 2.6.1 Individual random variables.- 2.6.2 Random vectors.- 2.6.3 Sequences of random variables.- 2.7 Complements.- 2.7.1 Measurability with respect to sub-?-algebras.- 2.7.2 Borel measurable functions.- 2.8 Exercises.- 3 Independence.- 3.1 Independent Random Variables.- 3.1.1 Fundamentals.- 3.1.2 Criteria for independence.- 3.1.3 Examples.- 3.2 Functions of Independent Random Variables.- 3.2.1 Transformation properties.- 3.2.2 Sums of independent random variables.- 3.3 Constructing Independent Random Variables.- 3.3.1 Finite families.- 3.3.2 Sequences.- 3.4 Independent Events.- 3.5 Occupancy Models.- 3.5.1 Four occupancy models.- 3.5.2 Occupancy numbers.- 3.5.3 Asymptotics.- 3.6 Bernoulli and Poisson Processes.- 3.6.1 Bernoulli processes.- 3.6.2 Poisson processes.- 3.7 Complements.- 3.7.1 Independent ?-algebras.- 3.7.2 Products of probability spaces.- 3.8 Exercises.- 4 Expectation.- 4.1 Definition and Fundamental Properties.- 4.1.1 Simple random variables.- 4.1.2 Positive random variables.- 4.1.3 Integrable random variables.- 4.1.4 Complex-valued random variables.- 4.2 Integrals with respect to Distribution Functions.- 4.2.1 Generalities.- 4.2.2 Discrete distribution functions.- 4.2.3 Absolutely continuous distribution functions.- 4.2.4 Mixed distribution functions.- 4.3 Computation of Expectations.- 4.3.1 Positive random variables.- 4.3.2 Integrable random variables.- 4.3.3 Functions of random variables.- 4.3.4 Functions of random vectors.- 4.3.5 Functions of independent random variables.- 4.3.6 Sums of independent random variables.- 4.4 LP Spaces and Inequalities.- 4.4.1 LPspaces.- 4.4.2 Key inequalities.- 4.5 Moments.- 4.5.1 Moments of random variables.- 4.5.2 Variance and standard deviation.- 4.5.3 Covariance and correlation.- 4.5.4 Moments of random vectors.- 4.5.5 Multivariate normal distributions.- 4.6 Complements.- 4.6.1 Integration with respect to Lebesgue measure.- 4.6.2 Expectation for product probabilities.- 4.7 Exercises.- 5 Convergence of Sequences of Random Variables.- 5.1 Modes of Convergence.- 5.1.1 Convergence of random variables as functions.- 5.1.2 Convergence of distribution functions.- 5.1.3 Alternative criteria.- 5.2 Relationships Among the Modes.- 5.2.1 Implications always valid.- 5.2.2 Counterexamples.- 5.2.3 Implications of restricted validity.- 5.2.4 Implications involving subsequences.- 5.3 Convergence under Transformations.- 5.3.1 Algebraic operations.- 5.3.2 Continuous mappings.- 5.4 Convergence of Random Vectors.- 5.4.1 Convergence of random vectors as functions.- 5.4.2 Convergence in distribution.- 5.4.3 Continuous mappings.- 5.5 Limit Theorems for Bernoulli Summands.- 5.5.1 Laws of large numbers.- 5.5.2 Central limit theorems.- 5.5.3 The Poisson limit theorem.- 5.5.4 Approximation of continuous functions.- 5.6 Complements.- 5.6.1 LP Convergence of random variables.- 5.7 Exercises.- 6 Characteristic Functions.- 6.1 Definition and Basic Properties.- 6.1.1 Fundamentals.- 6.1.2 Elementary properties.- 6.2 Inversion and Uniqueness Theorems.- 6.2.1 The inversion theorem.- 6.2.2 The uniqueness theorem.- 6.2.3 Specialized inversion theorems.- 6.3 Moments and Taylor Expansions.- 6.3.1 Calculation of moments known to exist.- 6.3.2 Establishing existence of moments.- 6.3.3 Taylor expansions of characteristic functions.- 6.4 Continuity Theorems and Applications.- 6.4.1 Convergence in distribution.- 6.4.2 The Levy continuity theorem.- 6.4.3 Application to classical limit theorems.- 6.5 Other Transforms.- 6.5.1 Characteristic functions of random vectors.- 6.5.2 Laplace transforms.- 6.5.3 Moment generating functions.- 6.5.4 Generating functions.- 6.6 Complements.- 6.6.1 Helly's theorem.- 6.7 Exercises.- 7 Classical Limit Theorems.- 7.1 Series of Independent Random Variables.- 7.1.1 Kolmogorov's inequality.- 7.1.2 The three series theorem.- 7.2 The Strong Law of Large Numbers.- 7.3 The Central Limit Theorem.- 7.3.1 The Lyapunov condition.- 7.3.2 The Lindeberg condition.- 7.4 The Law of the Iterated Logarithm.- 7.4.1 Normally distributed summands.- 7.4.2 More general versions.- 7.5 Applications of the Limit Theorems.- 7.5.1 Monte Carlo integration.- 7.5.2 Maximum likelihood estimation.- 7.5.3 Empirical distribution functions.- 7.5.4 Random sums of independent random variables.- 7.5.5 Renewal processes.- 7.6 Complements.- 7.6.1 The Berry-Esseen theorem.- 7.7 Exercises.- 8 Prediction and Conditional Expectation.- 8.1 Prediction in L2.- 8.1.1 The inner product and norm.- 8.1.2 L2 as metric space.- 8.1.3 Orthogonality and orthonormality.- 8.1.4 The orthogonal decomposition theorem.- 8.1.5 Computation of MMSE predictors.- 8.1.6 Linear prediction.- 8.2 Conditional Expectation Given a Finite Set of Random Variables.- 8.2.1 Basics.- 8.2.2 Examples.- 8.2.3 Conditional probability.- 8.3 Conditional Expectation for X?L2.- 8.3.1 Conditional expectation as MMSE prediction.- 8.3.2 Properties of conditional expectation.- 8.4 Positive and Integrable Random Variables.- 8.5 Conditional Distributions.- 8.5.1 Generalities.- 8.5.2 Discrete random variables.- 8.5.3 Absolutely continuous random variables.- 8.6 Computational Techniques.- 8.6.1 General results.- 8.6.2 Special cases.- 8.7 Complements.- 8.7.1 Mixed conditional distributions.- 8.7.2 Conditional expectation given a ?-algebra.- 8.8 Exercises.- 9 Martingales.- 9.1 Fundamentals.- 9.1.1 Definitions.- 9.1.2 Examples.- 9.1.3 Compositions and transformations.- 9.2 Stopping Times.- 9.3 Optional Sampling Theorems.- 9.3.1 Optional sampling theorems for martingales.- 9.3.2 Applications of optional sampling theorems.- 9.4 Martingale Convergence Theorems.- 9.4.1 Upcrossings and almost sure convergence.- 9.4.2 Almost sure convergence of submartingales.- 9.4.3 Almost sure convergence of martingales.- 9.4.4 Uniformly integrable martingales.- 9.5 Applications of Convergence Theorems.- 9.5.1 The Radon-Nikodym theorem.- 9.5.2 Zero-one laws.- 9.5.3 Likelihood ratios.- 9.6 Complements.- 9.6.1 Conditioning on Yo,...,YT.- 9.6.2 Martingales with respect to filtrations.- 9.6.3 Reversed martingales.- 9.7 Exercises.- A Notation.- B Named Objects.
- 巻冊次
-
: pbk ISBN 9781461269373
内容説明
This book offers a straightforward introduction to the mathematical theory of probability. It presents the central results and techniques of the subject in a complete and self-contained account. As a result, the emphasis is on giving results in simple forms with clear proofs and to eschew more powerful forms of theorems which require technically involved proofs. Throughout there are a wide variety of exercises to illustrate and to develop ideas in the text.
目次
Prelude: Random Walks.- The Model.- Random variables.- Probability.- First calculations.- Issues and Approaches.- Issues.- Approaches.- Tools.- Functional of the Random Walk.- Times of returns to the origin.- Numbers of returns to the origin.- First passage times.- Maxima.- Time spent positive.- Limit Theorems.- Summary.- 1 Probability.- 1.1 Random Experiments and Sample Spaces.- 1.1.1 Random experiments.- 1.1.2 Sample spaces.- 1.2 Events and Classes of Sets.- 1.2.1 Events.- 1.2.2 Basic set operations.- 1.2.3 Indicator functions.- 1.2.4 Operations on sequences of sets.- 1.2.5 Classes of sets closed under set operations.- 1.2.6 Generated classes.- 1.2.7 The monotone class theorem.- 1.2.8 Events, bis.- 1.3 Probabilities and Probability Spaces.- 1.3.1 Probability.- 1.3.2 Elementary properties.- 1.3.3 More advanced properties.- 1.3.4 Almost sure and null events.- 1.3.5 Uniqueness.- 1.4 Probabilities on R.- 1.4.1 Distribution functions.- 1.4.2 Discrete probabilities.- 1.4.3 Absolutely continuous probabilities.- 1.4.4 Mixed distributions.- 1.5 Conditional Probability Given a Set.- 1.6 Complements.- 1.6.1 The extended real numbers.- 1.6.2 Measures.- 1.6.3 Lebesgue measure.- 1.6.4 Singular probabilities on R.- 1.6.5 Representation of probabilities on R.- 1.7 Exercises.- 2 Random Variables.- 2.1 Fundamentals.- 2.1.1 Random variables.- 2.1.2 Random vectors.- 2.1.3 Stochastic processes.- 2.1.4 Complex-valued random variables.- 2.1.5 The -algebra generated by a random variable.- 2.1.6 Simplified criteria.- 2.2 Combining Random Variables.- 2.2.1 Algebraic operations.- 2.2.2 Limiting operations.- 2.2.3 Transformations.- 2.2.4 Approximation of positive random variables.- 2.2.5 Monotone class theorems.- 2.3 Distributions and Distribution Functions.- 2.3.1 Random variables.- 2.3.2 Random vectors.- 2.4 Key Random Variables and Distributions.- 2.4.1 Discrete random variables.- 2.4.2 Absolutely continuous random variables.- 2.4.3 Random vectors.- 2.5 Transformation Theory.- 2.5.1 Random variables.- 2.5.2 Random vectors.- 2.6 Random Variables with Prescribed Distributions.- 2.6.1 Individual random variables.- 2.6.2 Random vectors.- 2.6.3 Sequences of random variables.- 2.7 Complements.- 2.7.1 Measurability with respect to sub-?-algebras.- 2.7.2 Borel measurable functions.- 2.8 Exercises.- 3 Independence.- 3.1 Independent Random Variables.- 3.1.1 Fundamentals.- 3.1.2 Criteria for independence.- 3.1.3 Examples.- 3.2 Functions of Independent Random Variables.- 3.2.1 Transformation properties.- 3.2.2 Sums of independent random variables.- 3.3 Constructing Independent Random Variables.- 3.3.1 Finite families.- 3.3.2 Sequences.- 3.4 Independent Events.- 3.5 Occupancy Models.- 3.5.1 Four occupancy models.- 3.5.2 Occupancy numbers.- 3.5.3 Asymptotics.- 3.6 Bernoulli and Poisson Processes.- 3.6.1 Bernoulli processes.- 3.6.2 Poisson processes.- 3.7 Complements.- 3.7.1 Independent ?-algebras.- 3.7.2 Products of probability spaces.- 3.8 Exercises.- 4 Expectation.- 4.1 Definition and Fundamental Properties.- 4.1.1 Simple random variables.- 4.1.2 Positive random variables.- 4.1.3 Integrable random variables.- 4.1.4 Complex-valued random variables.- 4.2 Integrals with respect to Distribution Functions.- 4.2.1 Generalities.- 4.2.2 Discrete distribution functions.- 4.2.3 Absolutely continuous distribution functions.- 4.2.4 Mixed distribution functions.- 4.3 Computation of Expectations.- 4.3.1 Positive random variables.- 4.3.2 Integrable random variables.- 4.3.3 Functions of random variables.- 4.3.4 Functions of random vectors.- 4.3.5 Functions of independent random variables.- 4.3.6 Sums of independent random variables.- 4.4 LP Spaces and Inequalities.- 4.4.1 LPspaces.- 4.4.2 Key inequalities.- 4.5 Moments.- 4.5.1 Moments of random variables.- 4.5.2 Variance and standard deviation.- 4.5.3 Covariance and correlation.- 4.5.4 Moments of random vectors.- 4.5.5 Multivariate normal distributions.- 4.6 Complements.- 4.6.1 Integration with respect to Lebesgue measure.- 4.6.2 Expectation for product probabilities.- 4.7 Exercises.- 5 Convergence of Sequences of Random Variables.- 5.1 Modes of Convergence.- 5.1.1 Convergence of random variables as functions.- 5.1.2 Convergence of distribution functions.- 5.1.3 Alternative criteria.- 5.2 Relationships Among the Modes.- 5.2.1 Implications always valid.- 5.2.2 Counterexamples.- 5.2.3 Implications of restricted validity.- 5.2.4 Implications involving subsequences.- 5.3 Convergence under Transformations.- 5.3.1 Algebraic operations.- 5.3.2 Continuous mappings.- 5.4 Convergence of Random Vectors.- 5.4.1 Convergence of random vectors as functions.- 5.4.2 Convergence in distribution.- 5.4.3 Continuous mappings.- 5.5 Limit Theorems for Bernoulli Summands.- 5.5.1 Laws of large numbers.- 5.5.2 Central limit theorems.- 5.5.3 The Poisson limit theorem.- 5.5.4 Approximation of continuous functions.- 5.6 Complements.- 5.6.1 LP Convergence of random variables.- 5.7 Exercises.- 6 Characteristic Functions.- 6.1 Definition and Basic Properties.- 6.1.1 Fundamentals.- 6.1.2 Elementary properties.- 6.2 Inversion and Uniqueness Theorems.- 6.2.1 The inversion theorem.- 6.2.2 The uniqueness theorem.- 6.2.3 Specialized inversion theorems.- 6.3 Moments and Taylor Expansions.- 6.3.1 Calculation of moments known to exist.- 6.3.2 Establishing existence of moments.- 6.3.3 Taylor expansions of characteristic functions.- 6.4 Continuity Theorems and Applications.- 6.4.1 Convergence in distribution.- 6.4.2 The Levy continuity theorem.- 6.4.3 Application to classical limit theorems.- 6.5 Other Transforms.- 6.5.1 Characteristic functions of random vectors.- 6.5.2 Laplace transforms.- 6.5.3 Moment generating functions.- 6.5.4 Generating functions.- 6.6 Complements.- 6.6.1 Helly's theorem.- 6.7 Exercises.- 7 Classical Limit Theorems.- 7.1 Series of Independent Random Variables.- 7.1.1 Kolmogorov's inequality.- 7.1.2 The three series theorem.- 7.2 The Strong Law of Large Numbers.- 7.3 The Central Limit Theorem.- 7.3.1 The Lyapunov condition.- 7.3.2 The Lindeberg condition.- 7.4 The Law of the Iterated Logarithm.- 7.4.1 Normally distributed summands.- 7.4.2 More general versions.- 7.5 Applications of the Limit Theorems.- 7.5.1 Monte Carlo integration.- 7.5.2 Maximum likelihood estimation.- 7.5.3 Empirical distribution functions.- 7.5.4 Random sums of independent random variables.- 7.5.5 Renewal processes.- 7.6 Complements.- 7.6.1 The Berry-Esseen theorem.- 7.7 Exercises.- 8 Prediction and Conditional Expectation.- 8.1 Prediction in L2.- 8.1.1 The inner product and norm.- 8.1.2 L2 as metric space.- 8.1.3 Orthogonality and orthonormality.- 8.1.4 The orthogonal decomposition theorem.- 8.1.5 Computation of MMSE predictors.- 8.1.6 Linear prediction.- 8.2 Conditional Expectation Given a Finite Set of Random Variables.- 8.2.1 Basics.- 8.2.2 Examples.- 8.2.3 Conditional probability.- 8.3 Conditional Expectation for X?L2.- 8.3.1 Conditional expectation as MMSE prediction.- 8.3.2 Properties of conditional expectation.- 8.4 Positive and Integrable Random Variables.- 8.5 Conditional Distributions.- 8.5.1 Generalities.- 8.5.2 Discrete random variables.- 8.5.3 Absolutely continuous random variables.- 8.6 Computational Techniques.- 8.6.1 General results.- 8.6.2 Special cases.- 8.7 Complements.- 8.7.1 Mixed conditional distributions.- 8.7.2 Conditional expectation given a ?-algebra.- 8.8 Exercises.- 9 Martingales.- 9.1 Fundamentals.- 9.1.1 Definitions.- 9.1.2 Examples.- 9.1.3 Compositions and transformations.- 9.2 Stopping Times.- 9.3 Optional Sampling Theorems.- 9.3.1 Optional sampling theorems for martingales.- 9.3.2 Applications of optional sampling theorems.- 9.4 Martingale Convergence Theorems.- 9.4.1 Upcrossings and almost sure convergence.- 9.4.2 Almost sure convergence of submartingales.- 9.4.3 Almost sure convergence of martingales.- 9.4.4 Uniformly integrable martingales.- 9.5 Applications of Convergence Theorems.- 9.5.1 The Radon-Nikodym theorem.- 9.5.2 Zero-one laws.- 9.5.3 Likelihood ratios.- 9.6 Complements.- 9.6.1 Conditioning on Yo,...,YT.- 9.6.2 Martingales with respect to filtrations.- 9.6.3 Reversed martingales.- 9.7 Exercises.- A Notation.- B Named Objects.
- 巻冊次
-
: gw ISBN 9783540940715
内容説明
This is a textbook which will provide students with a straightforward introduction to the mathematical theory of probability. It is written with the aim of presenting the central results and techniques of the subject in a complete and self-contained account. As a result, the emphasis is on giving results in simple forms with clear proofs and to eschew more powerful forms of theorems which require technically involved proofs. Any graduate student who has a familiarity with real analysis will be able to use this text - measure theory is used only where necessary and undue abstraction is avoided. Throughout there are a wide variety of exercises to illustrate and to develop ideas in the text.
「Nielsen BookData」 より