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Statistics
Stochastic Processes
1. Foundations of Probability Theory
2. Introduction to Stochastic Processes
3. Discrete-Time Markov Chains
4. Poisson Processes
5. Continuous-Time Markov Chains
6. Renewal Theory
7. Martingales
8. Brownian Motion
9. Stochastic Calculus
10. Stationary Processes
11. Applications in Queueing Theory
12. Applications in Finance
13. Applications in Biology and Population Dynamics
14. Applications in Physics and Engineering
Discrete-Time Markov Chains
Fundamental Concepts
State Space
Finite State Spaces
Countably Infinite State Spaces
Transition Probabilities
One-Step Transition Probabilities
Transition Probability Matrix
Row-Stochastic Property
Markov Property
Memoryless Property
Formal Statement
Conditional Independence
Multi-Step Transitions
n-Step Transition Probabilities
Chapman-Kolmogorov Equations
Derivation
Matrix Formulation
Powers of Transition Matrix
Classification of States
Accessibility and Communication
Accessible States
Communicating States
Communication Relation
Communicating Classes
Equivalence Classes
Closed Classes
Open Classes
Irreducible Chains
Recurrence and Transience
First Return Times
Recurrent States
Transient States
Criteria for Recurrence
Positive and Null Recurrence
Expected Return Times
Positive Recurrent States
Null Recurrent States
Periodicity
Period of a State
Aperiodic States
Periodic Chains
Absorption and Hitting Probabilities
Absorbing States
Absorption Probabilities
First Passage Times
Hitting Probabilities
Fundamental Matrix
Long-Run Behavior
Limiting Distributions
Existence Conditions
Uniqueness Results
Stationary Distributions
Definition and Properties
Balance Equations
Calculation Methods
Ergodic Theorems
Ergodicity Conditions
Convergence Results
Time Averages
Special Classes and Applications
Time Reversibility
Detailed Balance Equations
Reversible Chains
Doubly Stochastic Matrices
Random Walks on Graphs
Gambler's Ruin Problem
Problem Formulation
Solution Methods
Probability of Ruin
Branching Processes
Galton-Watson Process
Extinction Probabilities
Critical Cases
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2. Introduction to Stochastic Processes
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4. Poisson Processes