Neurobiological Signals: Processing and Classification

Related lectures (32)

Page 3 of 4

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

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

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

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

Explores probability inequalities, convergence types, and moment generating functions for distribution approximation.

Explores invariant distributions, recurrent states, and convergence in Markov chains, including practical applications like PageRank in Google.

Explores Markov chains' properties, expectations, and recurrence in Poisson processes.

Explores Markov chains, transition matrices, distribution, and random walks.

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

Covers Markov chains, Metropolis algorithm, Glauber dynamics, and heat bath dynamics.