Theory of MCMC

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Related lectures (43)

Markov Chains: Ergodic Chains Examples Log in to Mediaspace to watch this video

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

Markov Chains: Ergodic Chains Examples Log in to Mediaspace to watch this video

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

Markov Chains: Reversibility & Convergence Log in to Mediaspace to watch this video

Covers Markov chains, focusing on reversibility, convergence, ergodicity, and applications.

Probability & Stochastic Processes Log in to Mediaspace to watch this video

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Markov Chain Monte Carlo: Detailed Balance & Neural Networks Log in to Mediaspace to watch this video

Covers Markov chain Monte Carlo and neural networks' role in quantum states representation and ground state approximation for frustrated spins systems.

Markov Chains: Ergodic Chains Examples Log in to Mediaspace to watch this video

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

Sampling: Signal Reconstruction and Aliasing Log in to Mediaspace to watch this video

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

Sampling: DT-time processing of CT signals Log in to Mediaspace to watch this video

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

Wireless Receivers: Time and Phase Offset Log in to Mediaspace to watch this video

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

Continuous-Time Markov Chains: Reversible Chains Log in to Mediaspace to watch this video

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

Signals, Instruments, and Systems Log in to Mediaspace to watch this video

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

Analog-Digital & Digital-Analog Converters Log in to Mediaspace to watch this video

Covers Analog-Digital and Digital-Analog conversion, including sampling, quantization, Flash converter, weighted current generation, and R2R network.

Markov Chain Monte Carlo Log in to Mediaspace to watch this video

Explains the Markov Chain Monte Carlo method and the Metropolis-Hastings algorithm for sampling.

Analog-to-Digital Conversion Log in to Mediaspace to watch this video

Explores analog-to-digital conversion principles, covering resolution, sampling rate, and conversion characteristics.

Nonlinear Equations: Convergence and Taylor Polynomials Log in to Mediaspace to watch this video

Explores nonlinear equations, emphasizing convergence and Taylor polynomials for function approximation.

Markov Chains: General Concepts Log in to Mediaspace to watch this video

Covers the general concepts of Markov chains and their applications in various fields.

Linear Regression: Estimation and Prediction Log in to Mediaspace to watch this video

Covers the basics of linear regression, focusing on estimation and prediction.

Machine Learning Fundamentals Log in to Mediaspace to watch this video

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Computational Protein Design: Principles and Challenges Log in to Mediaspace to watch this video

Covers the principles and challenges of computational protein design in bioengineering.

Digital Physics: Convergence and Error Analysis Log in to Mediaspace to watch this video

Discusses evaluation feedback, convergence, error analysis, and adaptive time steps in physics simulations.

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