Lectures related to Discrete-Time Markov Chains: Absorbing Chains Examples

Covers examples of absorbing Markov chains in discrete time.

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

Covers quantum eigenfunctions and the importance of A and B commuting for the same set of eigenfunctions.

Covers the Runge-Kutta method for approximating solutions of differential equations.

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

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

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

Covers the derivation of quantum Langevin equations and input-output formalism in open quantum systems.

Explores discrete-time Markov chains, emphasizing first passage time probabilities and minimal solutions.

Discusses discrete-time Markov chains and absorption time in communication systems.

Covers the measurement of observable eigenvalues and the importance of complete orthonormal sets.

Covers reversible continuous-time Markov chains and their properties.

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

Covers the analysis of continuous-time stochastic processes in the context of linear systems.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Explores Calderbank-Steane-Shor codes, classical codes, error correction, CSS codes, and protecting states from errors.

Covers the fundamental concepts of quantum mechanics, focusing on Hilbert spaces and operators.

Introduces quantum perturbation theory and its systematic approach to solving perturbed quantum systems.

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

Explores continuous-time Markov chains with a focus on birth and death processes.