Lecture

Computer Simulation: Early Days and Monte Carlo Method

Related lectures (29)

Explores molecular dynamics simulations under holonomic constraints, focusing on numerical integration and algorithm formulation.

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

Molecular Dynamics and Monte Carlo

Covers computational methods for molecular systems at finite temperature, emphasizing stochastic sampling and time evolution simulations.

Molecular Dynamics Basics

Covers the fundamentals of Molecular Dynamics, including the Ergodic Hypothesis, force fields, and periodic boundary conditions.

Molecular Dynamics and Monte Carlo: Computational Environment

Covers the computational environment for Molecular Dynamics and Monte Carlo exercises, emphasizing theoretical understanding over coding skills.

Law of Large Numbers: Strong Convergence

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Probability & Stochastic Processes

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

Statistical Physics: Systems Isolation

Explores statistical physics concepts in isolated systems, focusing on entropy and disorder.

Quasi-Stationary Distribution: Molecular Dynamics Modeling

Explores the quasi-stationary distribution approach in molecular dynamics modeling, covering Langevin dynamics, metastability, and kinetic Monte Carlo models.

Markov Chains: Theory and Applications

Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.

Markov Chain: Configuration Sampling

Introduces the concept of a Markov process and chain in configuration sampling.

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Monte Carlo Simulations

Covers the theory and practical aspects of Monte Carlo simulations in molecular dynamics, including ensemble averages and Metropolis algorithm.

Efficiency of Sampling: Ergodicity and Autocorrelation Functions

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

Elements of Statistics: Probability, Distributions, and Estimation

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Markov Chains: Ergodicity and Stationary Distribution

Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.

Molecular dynamics and integrators

Explores molecular dynamics, integrators, trajectory generation, and error monitoring in atomistic modeling.

Expected Number of Visits in State

Covers the criterion for recurrence in infinite chains based on the expected number of visits in a state.

Theory of MCMC

Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.

Stochastic Models for Communications

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.