Chapter 1: Introduction to Stochastic Processes
1.1. Sequences of random variables
1.2. The notion of stochastic process
1.6. Continuous-time Markov processes
Chapter 2: Simple Stochastic Models
2.5. Birth and death processes
Chapter 3: Elements of Markov Modeling
3.1. Markov models: ideas, history, applications
3.2. The discrete-time Ehrenfest model
3.3. Markov models in genetics
3.5. Reliability of Markov models
4.1. Fundamental concepts and examples
4.3. Modified renewal processes
4.6. The risk problem of an insurance company
4.8. Alternating renewal processes
4.9. Superposition of renewal processes
5.3. First-passage times and state classification
5.7. Digital communication channels
6.1. The Bienaymé-Galton-Watson model
6.2. Generalizations of the B-G-W model
Chapter 7: Optimal Stopping Models
7.1. The classic optimal stopping problem
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