Stochastic Models: Absorbing Markov Chains Examples

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (28)

Page 2 of 3

Markov Chains: Definitions and State Probabilities

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

Normal Distribution: Properties and Calculations

Covers properties and calculations related to the normal distribution, including probabilities and quantiles.

Stochastic Models for Communications

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

Probability and Statistics

Explores joint random variables, conditional density, and independence in probability and statistics.

Variance and Covariance: Properties and Examples

Explores variance, covariance, and practical applications in statistics and probability.

Statistical Analysis: Boxplot and Normal Distribution

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

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.

Probability and Statistics

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Probability and Statistics: Fundamental Theorems

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Law of Large Numbers: Strong Convergence

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.