Theory of MCMC

Related lectures (31)

Explores the efficiency of sampling in molecular dynamics, focusing on ergodicity and autocorrelation functions.

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

Covers the Fourier transform of sampled signals, reconstruction, and harmonic response.

Covers the importance of sampling, signal reconstruction, and aliasing in digital representation.

Covers the importance of sampling in signal processing, including the sampling theorem and signal reconstruction.

Covers the impact and compensation of time and phase offset in wireless receivers.

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

Covers the theory of digital signal processing, including sampling, transformation methods, digitization, and PID controllers.

Explores signals, instruments, and systems, covering ADC, Fourier Transform, sampling, signal reconstruction, aliasing, and anti-alias filters.

Explores the PageRank algorithm within Markov chains, emphasizing ergodicity and convergence for web page ranking.

Explores Monte Carlo moves in simulation, including trial moves and biased moves, comparing Monte Carlo with Molecular Dynamics.

Page 2 of 2