Biased Monte Carlo Markov Chain

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Probabilistic Estimation in Spin Glass Card Game

Explores probabilistic estimation in the Spin Glass Card game, emphasizing room splitting based on a given parameter.

Markov Chains: Simulation and Optimization

Explores Markov chains, Metropolis-Hastings, and simulation for optimization purposes, highlighting the significance of ergodicity in efficient variable simulation.

MCMC Examples and Error Estimation

Covers Markov Chain Monte Carlo examples and error estimation methods.

Efficient Stochastic Numerical Methods

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

Bayesian Estimation: Unsupervised Learning & MCMC

Explores Bayesian estimation for unsupervised learning and MCMC, using a Spin Glass Card game example.

Markov Chains and Algorithm Applications

Covers the application of Markov chains and algorithms for function optimization and graph colorings.

Spin Glasses and Bayesian Estimation

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Probability & Stochastic Processes

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

Molecular Dynamics and Monte Carlo: Computational Environment

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

Stochastic Simulation: Markov Chains and Monte Carlo

Covers Markov chains, Monte Carlo methods, low discrepancy sequences, and multidimensional integrals computation.