Markov Chain Monte Carlo: Sampling High-Dimensional Distributions

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

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

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

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

Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.

Introduces maximum entropy inference for finding optimal probability distributions with minimal extra structure.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.