Lectures related to Quasi-Stationary Distribution: Molecular Dynamics Modeling

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.

Efficient Stochastic Numerical Methods

Explores efficient stochastic numerical methods for modeling and learning, covering topics like the Analytical Engine and kinase inhibitors.

Turbulence: Numerical Flow Simulation

Explores turbulence characteristics, simulation methods, and modeling challenges, providing guidelines for choosing and validating turbulence models.

Computer Simulation: Early Days and Monte Carlo Method

Explores the early days of computer simulation, focusing on the Monte Carlo method and its evolution in scientific research.

Probability & Stochastic Processes

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

Markov Chains: Applications and Sampling Methods

Covers the basics of Markov chains and their algorithmic applications.

Molecular dynamics under constraints

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

Theory of MCMC

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

Untitled

Molecular Dynamics and Monte Carlo: Computational Environment

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

Markov Chain Monte Carlo

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

Untitled

Molecular Dynamics and Monte Carlo

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

Explicit Stabilised Methods: Applications to Bayesian Inverse Problems

Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.

MCMC Examples and Error Estimation

Covers Markov Chain Monte Carlo examples and error estimation methods.

Convergence of Langevin Monte Carlo: Tail Growth and Smoothness

Explores the convergence of Langevin Monte Carlo algorithms under different growth rates and smoothness conditions, emphasizing fast convergence for a wide class of potentials.

Markov Chain Monte Carlo: Sampling and Convergence

Explores Markov Chain Monte Carlo for sampling high-dimensional distributions and optimizing functions using the Metropolis-Hastings algorithm.

Monte Carlo: Optimization and Estimation

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

Monte Carlo Markov Chains

Covers the theory of Markov chains and Monte Carlo methods.

Quantum to Classical Mechanics: MD & MC Simulations

Covers Molecular Dynamics and Monte Carlo Simulations, transitioning from Quantum to Classical Mechanics in computational chemistry.