Open Menu
- 0 Preface
- 1 Basic Concepts
- 2 Combinatorics: Counting Methods
- 3 Discrete Random Variables
- 4 Continuous and Mixed Random Variables
- 5 Joint Distributions
- 6 Multiple Random Variables
- 7 Limit Theorems and Convergence of Random Variables
- 7.0 Introduction
- 7.1 Limit Theorems
- 7.2 Convergence of Random Variables
- 7.2.0 Convergence of Random Variables
- 7.2.1 Convergence of Sequence of Numbers
- 7.2.2 Sequence of Random Variables
- 7.2.3 Different Types of Convergence for Sequences of Random Variables
- 7.2.4 Convergence in Distribution
- 7.2.5 Convergence in Probability
- 7.2.6 Convergence in Mean
- 7.2.7 Almost Sure Convergence
- 7.2.8 Solved Problems
- 7.3 Problems
- 8 Statistical Inference I: Classical Methods
- 9 Statistical Inference II: Bayesian Inference
- 9.1 Bayesian Inference
- 9.1.0 Bayesian Inference
- 9.1.1 Prior and Posterior
- 9.1.2 Maximum A Posteriori (MAP) Estimation
- 9.1.3 Comparison to ML Estimation
- 9.1.4 Conditional Expectation (MMSE)
- 9.1.5 Mean Squared Error (MSE)
- 9.1.6 Linear MMSE Estimation of Random Variables
- 9.1.7 Estimation for Random Vectors
- 9.1.8 Bayesian Hypothesis Testing
- 9.1.9 Bayesian Interval Estimation
- 9.1.10 Solved Problems
- 9.2 Problems
- 9.1 Bayesian Inference
- 10 Introduction to Random Processes
- 11 Some Important Random Processes
- 12 Introduction to Simulation Using MATLAB
- 13 Introduction to Simulation Using R
- 14 Introduction to Simulation Using Python
- 15 Recursive Methods
- Appendix
- Bibliography
Introduction to Probability by Hossein Pishro-Nik is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License