Analog Regulator Sampling Methods

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Page 3 of 4

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Theory of MCMC

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

Sampling Complex Exponentials

Covers the sampling of complex exponentials and the challenges of reconstruction in different scenarios.

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.

Generative Models: Crash Course

Offers a crash course on generative models, covering exponential families, sampling methods, and Metropolis algorithm.

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.

Data Issues in Research

Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.

Sampling: Inference and Statistics

Explores sampling, inferential statistics, and effective experimentation in statistics.

Transfer Functions and Control Algorithms

Explores transfer functions, control algorithms, and system transformations in discrete and analog systems, with practical exercises included.

Powder Characterisation: Physical, Chemical, Morphology

Explores the physical, chemical, and morphological analysis of powders, emphasizing sampling accuracy and quality control.