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Lecture
Birth & Death Chains: Analysis & Probabilities
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Related lectures (29)
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Stochastic Models for Communications: Discrete-Time Markov Chains - First Passage Time
Explores discrete-time Markov chains, emphasizing first passage time probabilities and minimal solutions.
Stochastic Models: Absorbing Markov Chains Examples
Covers examples of absorbing Markov chains in discrete time.
Stochastic Models for Communications: Discrete-Time Markov Chains - Absorption Time
Discusses discrete-time Markov chains and absorption time in communication systems.
Discrete-Time Markov Chains: Definitions
Covers the definitions and state probabilities of discrete-time Markov chains.
Discrete-Time Markov Chains: Definitions
Covers the definitions and state probabilities of discrete-time Markov chains.
Probability & Stochastic Processes
Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.
Sunny Rainy Source: Markov Model
Explores a first-order Markov model using a sunny-rainy source example, demonstrating how past events influence future outcomes.
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.
Markov Chains: Definitions and State Probabilities
Covers the definitions and state probabilities of discrete-time Markov chains.
Probability and Statistics
Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.
