Lectures related to Diffusion Approximation: RTE

Monte Carlo Methods in Thermal Radiation: Applications and Solutions

Discusses the application of Monte Carlo methods to thermal radiation and energy transfer in participating media.

Explores the Radiative Transport Equation in tissue optics, covering radiance, photon distribution, and numerical solutions like Monte Carlo simulations.

Radiative Exchange: Specular View Factors

Covers specular view factors, radiative exchange, energy transfer, and numerical integration methods in thermal radiation.

Markov chains

Covers Markov chains, Monte Carlo sampling, isotropy, and the curse of dimensionality.

Max Entropy and Monte Carlo

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

Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Markov Chain Monte Carlo

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

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.

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 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.

Monte Carlo Methods: Thermal Radiation Applications

Discusses the application of Monte Carlo methods in thermal radiation analysis, focusing on probability functions and numerical integration techniques.

Numerical Simulation of SDEs: Monte Carlo & Optimal Control

Covers Monte Carlo methods, variance reduction, and stochastic optimal control, exploring simulation techniques, efficiency, and investment dynamics.

Probability & Stochastic Processes

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

Simulation & Optimization: Poisson Process & Random Numbers

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.

Markov Chain Monte Carlo: Rejection Sampling

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

Monte Carlo Markov Chains

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

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.

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.

Stochastic Simulation: Introduction and G/G/1 Queueing Model

Covers the stochastic simulations course, G/G/1 queueing model, computational finance, statistics, physics, and Bayesian inference.