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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
Continuous-Time Markov Chains
Basic Framework
State Space and Time Parameter
Transition Functions
Markov Property in Continuous Time
Right-Continuity of Sample Paths
Construction and Properties
Holding Times
Exponential Distribution
Memoryless Property
Rate Parameters
Jump Chain
Embedded Discrete-Time Chain
Jump Probabilities
Generator Matrix
Definition and Properties
Q-Matrix Structure
Relationship to Transition Rates
Kolmogorov Equations
Forward Equations
Derivation
Matrix Form
Backward Equations
Derivation
Matrix Form
Solutions and Uniqueness
Classification of States
Communicating Classes
Recurrence and Transience
Positive and Null Recurrence
Irreducibility
Long-Run Behavior
Limiting Probabilities
Stationary Distribution
Balance Equations
Global Balance
Detailed Balance
Ergodic Properties
Birth-and-Death Processes
Definition and Structure
Birth Rates and Death Rates
Generator Matrix Structure
Equilibrium Distribution
Product Form Solution
Normalization
Special Cases
Pure Birth Process
Pure Death Process
Linear Birth-Death Process
Queueing Applications
M/M/1 Queue
Model Description
Steady-State Analysis
Performance Measures
M/M/s Queue
Multiple Servers
Steady-State Distribution
Blocking Probabilities
M/M/∞ Queue
M/M/s/K Queue
Time Reversibility
Detailed Balance Conditions
Reversible Processes
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6. Renewal Theory