Monte Carlo Methods: Metropolis Algorithm

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Importance Sampling: Change and Distribution

Explores importance sampling in Monte Carlo calculations, emphasizing variable changes and distribution selection for efficiency.

Biased Monte Carlo Markov Chain

Explores Biased Monte Carlo Markov Chain, including Bayes-optimal estimation and Metropolis-Hastings algorithm.

Molecular Dynamics and Monte Carlo

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

Exact Simulation of Coupled Wright-Fisher Diffusions

Explores the exact simulation of coupled Wright-Fisher diffusions, covering population genetics, interacting loci networks, and diffusion challenges.

Monte Carlo Simulations: Photon Gas & LJ Potential

Covers importance sampling, detailed balance, and Metropolis Monte Carlo simulations in photon gas and LJ systems.

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 Simulation: Lennard-Jones Liquid

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

Quantum Monte Carlo: Basic Concepts

Covers the basic concepts of Quantum Monte Carlo and its application in spin chains, Markov chains, and Monte Carlo simulations.

Density of States and Bayesian Inference in Computational Mathematics

Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.

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