- Book
- 1 online resource.
- Cover; Title Page; Copyright Page; Dedication; Preface; Contents; Introduction; Chapter 1 Data Reduction; 1.1 Graphical Displays; 1.2 Numerical Summaries; 1.3 Data Observed in Pairs; 1.4 Summary for Chapter 1; Chapter 2 Elements of Probability Theory; 2.1 Random Events; 2.1.1 Sample Space and Events; 2.1.2 Probability Measure; 2.1.3 Simple Probabilities of Events; 2.1.4 Summary; 2.2 Random Variables and Distributions; 2.2.1 Random Variables; 2.2.2 Jointly Distributed Random Variables; 2.3 Derived Distributions; 2.3.1 One-variable Transformations: Y = g(X).
- 2.3.2 Functions of Two Random Variables2.3.3 Elementary Simulation; 2.3.4 Summary; 2.4 Moments and Expectation; 2.4.1 Moments of a Random Variable; 2.4.2 Expectation of a Function of a Random Variable; 2.4.3 Expectation and Jointly Distributed Random Variables; 2.4.4 Approximate Moments and Distributions of Functions; 2.4.5 Summary; 2.5 Summary for Chapter 2; Chapter 3 Common Probabilistic Models; 3.1 Models from Simple Discrete Random Trials; 3.1.1 A Single Trial: The Bernoulli Distribution; 3.1.2 Repeated Trials: The Binomial Distribution.
- 3.1.3 Repeated Trials: The Geometric and Negative Binomial Distributions3.1.4 Summary; 3.2 Models from Random Occurrences; 3.2.1 Counting Events: The Poisson Distribution; 3.2.2 Time between Events: The Exponential Distribution; 3.2.3 Time to the kth Event: The Gamma Distribution; 3.2.4 Summary; 3.3 Models from Limiting Cases; 3.3.1 The Model of Sums: The Normal Distribution; 3.3.2 The Model of Products: The Lognormal Distribution; 3.3.3 The Model of Extremes: The Extreme Value Distributions; 3.3.4 Summary; 3.4 Additional Common Distributions.
- 3.4.1 The Equally Likely Model: The Rectangular or Uniform Distribution3.4.2 The Beta Distribution; 3.4.3 Some Normal Related Distributions: Chi-square, Chi, t, and F; 3.4.4 Summary; 3.5 Modified Distributions; 3.5.1 Shifted and Transformed Distributions; 3.5.2 Truncated and Censored Distributions; 3.5.3 Compound Distributions; 3.5.4 Summary; 3.6 Multivariate Models; 3.6.1 Counting Multiple Events: The Multinomial Distribution; 3.6.2 The Multivariate Normal Distribution; 3.6.3 Summary; 3.7 Markov Chains; 3.7.1 Simple Markov Chains; 3.7.2 Two-state Homogeneous Chains.
- 3.7.3 Multistate Markov Chains3.7.4 Summary; 3.8 Summary for Chapter 3; Chapter 4 Probabilistic Models and Observed Data; 4.1 Estimation of Model Parameters; 4.1.1 The Method of Moments; 4.1.2 The Properties of Estimators: Their First- and Second-order Moments; 4.1.3 The Distributions of Estimators and Confidence-interval Estimation; 4.1.4 The Method of Maximum Likelihood; 4.1.5 Summary; 4.2 Significance Testing; 4.2.1 Hypothesis Testing; 4.2.2 Some Common Hypothesis Tests; 4.2.3 Summary; 4.3 Statistical Analysis of Linear Models; 4.3.1 Linear Models.
- Cover; Title Page; Copyright Page; Dedication; Preface; Contents; Introduction; Chapter 1 Data Reduction; 1.1 Graphical Displays; 1.2 Numerical Summaries; 1.3 Data Observed in Pairs; 1.4 Summary for Chapter 1; Chapter 2 Elements of Probability Theory; 2.1 Random Events; 2.1.1 Sample Space and Events; 2.1.2 Probability Measure; 2.1.3 Simple Probabilities of Events; 2.1.4 Summary; 2.2 Random Variables and Distributions; 2.2.1 Random Variables; 2.2.2 Jointly Distributed Random Variables; 2.3 Derived Distributions; 2.3.1 One-variable Transformations: Y = g(X).
- 2.3.2 Functions of Two Random Variables2.3.3 Elementary Simulation; 2.3.4 Summary; 2.4 Moments and Expectation; 2.4.1 Moments of a Random Variable; 2.4.2 Expectation of a Function of a Random Variable; 2.4.3 Expectation and Jointly Distributed Random Variables; 2.4.4 Approximate Moments and Distributions of Functions; 2.4.5 Summary; 2.5 Summary for Chapter 2; Chapter 3 Common Probabilistic Models; 3.1 Models from Simple Discrete Random Trials; 3.1.1 A Single Trial: The Bernoulli Distribution; 3.1.2 Repeated Trials: The Binomial Distribution.
- 3.1.3 Repeated Trials: The Geometric and Negative Binomial Distributions3.1.4 Summary; 3.2 Models from Random Occurrences; 3.2.1 Counting Events: The Poisson Distribution; 3.2.2 Time between Events: The Exponential Distribution; 3.2.3 Time to the kth Event: The Gamma Distribution; 3.2.4 Summary; 3.3 Models from Limiting Cases; 3.3.1 The Model of Sums: The Normal Distribution; 3.3.2 The Model of Products: The Lognormal Distribution; 3.3.3 The Model of Extremes: The Extreme Value Distributions; 3.3.4 Summary; 3.4 Additional Common Distributions.
- 3.4.1 The Equally Likely Model: The Rectangular or Uniform Distribution3.4.2 The Beta Distribution; 3.4.3 Some Normal Related Distributions: Chi-square, Chi, t, and F; 3.4.4 Summary; 3.5 Modified Distributions; 3.5.1 Shifted and Transformed Distributions; 3.5.2 Truncated and Censored Distributions; 3.5.3 Compound Distributions; 3.5.4 Summary; 3.6 Multivariate Models; 3.6.1 Counting Multiple Events: The Multinomial Distribution; 3.6.2 The Multivariate Normal Distribution; 3.6.3 Summary; 3.7 Markov Chains; 3.7.1 Simple Markov Chains; 3.7.2 Two-state Homogeneous Chains.
- 3.7.3 Multistate Markov Chains3.7.4 Summary; 3.8 Summary for Chapter 3; Chapter 4 Probabilistic Models and Observed Data; 4.1 Estimation of Model Parameters; 4.1.1 The Method of Moments; 4.1.2 The Properties of Estimators: Their First- and Second-order Moments; 4.1.3 The Distributions of Estimators and Confidence-interval Estimation; 4.1.4 The Method of Maximum Likelihood; 4.1.5 Summary; 4.2 Significance Testing; 4.2.1 Hypothesis Testing; 4.2.2 Some Common Hypothesis Tests; 4.2.3 Summary; 4.3 Statistical Analysis of Linear Models; 4.3.1 Linear Models.
eReserve
eReserve | Status |
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Instructor's copy | |
(no call number) | Unknown |
CEE-203-01
- Course
- CEE-203-01 -- Probabilistic Models in Civil Engineering
- Instructor(s)
- Baker, Jack Wesley