Lectures related to Markov Chains and Algorithm Applications

Discusses innovation taxonomies, value generation, distribution, and the concept of soft innovation in various industries.

Covers Markov Chain Monte Carlo examples and error estimation methods.

Compares classic and fractional factorial designs, covering main effects, interactions, MATLAB generation, and chemical reactor analysis.

Explores Markov chains, Ising Model, Metropolis algorithm, and Glauber dynamics.

Explores the lower bound on total variation distance in Markov chains and its implications on mixing time.

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

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

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

Explores energy systems optimization through linearization and practical examples.