Lectures related to Monte Carlo Integration: Correlated Sampling

Monte Carlo Integration: Uncorrelated Sampling

Explores Monte Carlo integration through uncorrelated sampling and its error scaling with the number of points used.

Monte Carlo Simulation: Lennard-Jones Liquid

Covers the Monte Carlo simulation of a Lennard-Jones liquid.

Probability & Stochastic Processes

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

Smarter Energy Future: Computing and Grid Integration

Explores computing integration in energy systems, smart grids, datacenter efficiency, and lithium-air batteries.

Monte Carlo Simulation: Lennard-Jones Liquid

Explores Monte Carlo simulation of a Lennard-Jones liquid and the importance of sampling through correlated sampling.

Markov Chains: Applications and Sampling Methods

Covers the basics of Markov chains and their algorithmic applications.

Markov Chain Monte Carlo: Rejection Sampling

Explores rejection sampling for generating sample values from a target distribution, along with Bayesian inference using MCMC.

Correlated and Uncorrelated Sampling

Explains correlated and uncorrelated sampling for generating random variables with given weight functions.

Explores correlated sampling, weight functions, Markovian chains, ergodicity, and walkers.

Monte Carlo Markov Chains

Covers Monte Carlo Markov Chains and sampling algorithms for iterative trial configurations.

Markov Chains and Algorithm Applications

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Monte Carlo: Optimization and Estimation

Explores optimization and estimation in Monte Carlo methods, emphasizing Bayes-optimal groups and estimators.

Errors in Correlated Sampling

Explains errors in correlated and uncorrelated sampling, correlation function, time, and blocking analysis.

Computational Physics: A Woman's Contribution to Molecular Dynamics

Delves into Mary Ann Mansigh Karlsen's pivotal role in computational physics and her lasting impact on the field.

Challenge of even bigger models

Explores simulating large-scale neural network models and optimizing memory efficiency in neural simulations using NEURON and CoreNEURON.

Introduction to Multiprocessor Architecture

Covers multiprocessor architecture, sustainable computing, AI model training impact, and parallel programming fundamentals.

Max Entropy and Monte Carlo

Explores max entropy, Shannon's entropy, Lagrange multipliers, and Monte Carlo sampling techniques.

Gibbs Sampling: Simulated Annealing

Covers the concept of Gibbs sampling and its application in simulated annealing.

Monte-Carlo Integration: Approximation and Variance

Explores Monte-Carlo integration for approximating expectations and variances using random sampling and discusses error components in conditional choice models.

Errors in Sampling: Correlation and Time Analysis

Explores errors in sampling, correlation functions, time analysis, and blocking techniques.