Probability
-15%
portes grátis
Probability
Modeling and Applications to Random Processes
Miller, Gregory K.
John Wiley & Sons Inc
09/2006
488
Dura
Inglês
9780471458920
15 a 20 dias
Written by an enthusiastic educator known for his dynamic teaching, Probability: Modeling and Applications to Random Processes focuses on modeling outcomes of random variables and random processes.
Preface. To the Student. To the Instructor. Coverage. Acknowledgments. Chapter 1. Modeling. 1.1 Choice and Chance. 1.2 The Model Building Process. 1.3 Modeling in the Mathematical Sciences. 1.4 A First Look at a Probability Model: The Random Walk. 1.5 Brief Applications of Random Walks. Exercises. Chapter 2. Sets and Functions. 2.1 Operations with Sets. 2.2 Functions. 2.3 The Probability Function and the Axioms of Probability. 2.4 Equally Likely Sample Spaces and Counting Rules. Rules. Exercises. Chapter 3. Probility Laws I: Building on the Axioms. 3.1 The Complement Rule. 3.2 The Addition Rule. 3.3 Extensions and Additional Results. Exercises. Chapter 4. Probility Laws II: Results of Conditioning. 4.1 Conditional Probability and the Multiplication Rule. 4.2 Independent Events. 4.3 The Theorem of Total Probabilities and Bayes' Rule. 4.4 Problems of Special Interest: Effortful Illustrations of the Probability Laws. Exercises. Chapter 5. Random Variables and Stochastic Processes. 5.1 Roles and Types of Random Variables. 5.2 Expectation. 5.3 Roles, Types, and Characteristics of Stochastic Processes. Exercises. Chapter 6. Discrete Random Variables and Applications in Stochastic Processes. 6.1 The Bernoulli and Binomial Models. 6.2 The Hypergeometric Model. 6.3 The Poisson Model. 6.4 The Geometric and Negative Binomial. Models. Exercises. Chapter 7. Continuous Random Variables and Applications in Stochastic Processes. 7.1 The Continuous Uniform Model. 7.2 The Exponential Model. 7.3 The Gamma Model. 7.4 The Normal Model. Chapter 8. Covariance and Correlation Among Random Variables. 8.1 Joint, Marginal and Conditional Distributions. 8.2 Covariance and Correlation. 8.3 Brief Examples and Illustrations in Stochastic Processes and Times Series. Exercises. Bibliography. Tables. Index.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Written by an enthusiastic educator known for his dynamic teaching, Probability: Modeling and Applications to Random Processes focuses on modeling outcomes of random variables and random processes.
Preface. To the Student. To the Instructor. Coverage. Acknowledgments. Chapter 1. Modeling. 1.1 Choice and Chance. 1.2 The Model Building Process. 1.3 Modeling in the Mathematical Sciences. 1.4 A First Look at a Probability Model: The Random Walk. 1.5 Brief Applications of Random Walks. Exercises. Chapter 2. Sets and Functions. 2.1 Operations with Sets. 2.2 Functions. 2.3 The Probability Function and the Axioms of Probability. 2.4 Equally Likely Sample Spaces and Counting Rules. Rules. Exercises. Chapter 3. Probility Laws I: Building on the Axioms. 3.1 The Complement Rule. 3.2 The Addition Rule. 3.3 Extensions and Additional Results. Exercises. Chapter 4. Probility Laws II: Results of Conditioning. 4.1 Conditional Probability and the Multiplication Rule. 4.2 Independent Events. 4.3 The Theorem of Total Probabilities and Bayes' Rule. 4.4 Problems of Special Interest: Effortful Illustrations of the Probability Laws. Exercises. Chapter 5. Random Variables and Stochastic Processes. 5.1 Roles and Types of Random Variables. 5.2 Expectation. 5.3 Roles, Types, and Characteristics of Stochastic Processes. Exercises. Chapter 6. Discrete Random Variables and Applications in Stochastic Processes. 6.1 The Bernoulli and Binomial Models. 6.2 The Hypergeometric Model. 6.3 The Poisson Model. 6.4 The Geometric and Negative Binomial. Models. Exercises. Chapter 7. Continuous Random Variables and Applications in Stochastic Processes. 7.1 The Continuous Uniform Model. 7.2 The Exponential Model. 7.3 The Gamma Model. 7.4 The Normal Model. Chapter 8. Covariance and Correlation Among Random Variables. 8.1 Joint, Marginal and Conditional Distributions. 8.2 Covariance and Correlation. 8.3 Brief Examples and Illustrations in Stochastic Processes and Times Series. Exercises. Bibliography. Tables. Index.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
