A second course in probability / Sheldon M. Ross and Erol A. Peköz.

1. Measure Theory and Laws of Large Numbers
Introduction
A Non-Measurable Event
Countable and Uncountable Sets
Probability Spaces
Random Variables
Expected Value
Almost Sure Convergence and the Dominated Convergence Theorem
Convergence in Probablitiy and in Distribution
Law of Large Numbers and Ergodic Theorem
Exercises
2. Stein's Method and Central Limit Theorems
Introduction
Coupling
Poisson Approximation and Le Cam's Theorem
The Stein-Chen Method
Stein's Method for the Geometric Distribution
Stein's Method for the Normal Distribution
Exercises
3. Conditional Expectation and Martingales
Introduction
Conditional Expectation
Martingales
The Martingale Stopping Theorem
The Hoeffding-Azuma Inequality
Submartingales, Supermartingales, and a Convergence Theorem
Exercises
4. Bounding Probabilities and Expectations
Introduction
Jensen's Inequality
Probability Bounds via the Importance Sampling Identity
Chernoff Bounds
Second Moment and Conditional Expectation Inequalities
The Min-Max Identity and Bounds on the Maximum
Stochastic Orderings
Exercises
5. Markov Chains
Introduction
The Transition Matrix
The Strong Markov Property
Classification of States
Stationary and Limiting Distributions
Time Reversibility
A Mean Passage Time Bound
Exercises
6. Renewal Theory
Introduction
Some Limit Theorems of Renewal Theory
Renewal Reward Processes
6.3.1 Queueing Theory Applications of Renewal Reward Processes
Blackwell's Theorem
The Poisson Process
Exercises
7. Brownian Motion
Introduction
Continuous Time Martingales
Construction Brownian Motion
Embedding Variables in Brownian Motion
The Central Limit Theorem
Exercises.