Discretisation of Continuous Time Systems

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Transfer Functions and Control Algorithms

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

Pizza Making Process

Covers the process of making pizza, sampling, averages, dispersion, residuals, and normal distribution.

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.

Sampling Complex Exponentials

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

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: Inference and Statistics

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

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.

Review Session: Module 1

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Stochastic Simulation: Metropolis-Hastings Algorithm

Covers the Metropolis-Hastings algorithm and convergence diagnostics in stochastic simulation, focusing on sampling and proposal generation.

Filtering before Sampling

Emphasizes the necessity of filtering signals before sampling to prevent undersampling effects.