Lectures related to Continuous-Time Markov Chains: Birth and Death Processes

Continuous-Time Markov Chains: Reversible Chains

Covers reversible continuous-time Markov chains and their properties.

Stochastic Models: Absorbing Markov Chains Examples

Covers examples of absorbing Markov chains in discrete time.

Discrete-Time Markov Chains: Absorbing Chains Examples

Explores examples of absorbing chains in discrete-time Markov chains, focusing on transition probabilities.

Continuous-Time Markov Chains: Reversible Chains

Covers continuous-time Markov chains, focusing on reversible chains and their properties.

Discrete-Time Markov Chains: Absorbing Chains Examples

Covers examples of absorbing chains in discrete-time Markov chains.

Stochastic Simulation: Markov Processes Generation

Covers the generation of Markov processes and Poisson processes in stochastic simulation.

Stochastic Processes: Markov Chains

Covers stochastic processes, focusing on Markov chains and their applications in real-world scenarios.

Markov Chains: Introduction and Properties

Covers the introduction and properties of Markov chains, including transition matrices and stochastic processes.

Bonus Malus System: Transition Probabilities

Explores the Bonus Malus system for insurance premiums and Markov chain transition probabilities.

Markov Chains: Basics and Applications

Introduces Markov chains, covering basics, generation algorithms, and applications in random walks and Poisson processes.

Markov Chains: Definitions and State Probabilities

Covers the definitions and state probabilities of discrete-time Markov chains.

Hidden Markov Models: Primer

Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.

Generation of Markov Processes

Covers the generation of Markov processes and Markov chains, including transition matrices and stochastic matrices.

Markov Chains: Theory and Applications

Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.

Continuous-Time Markov Chains: Birth and Death Processes

Covers the theory of continuous-time Markov chains, focusing on birth and death processes.

Stochastic Models for Communications

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Discrete-Time Markov Chains: Definitions

Covers the definitions and state probabilities of discrete-time Markov chains.

Stochastic Models for Communications: Discrete-Time Markov Chains - First Passage Time

Explores discrete-time Markov chains, emphasizing the concept of first passage time in communication systems.

Applied Probability & Stochastic Processes

Covers applied probability, Markov chains, and stochastic processes, including transition matrices, eigenvalues, and communication classes.