Modeling Neurobiological Signals: Spikes & Firing Rate

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Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Explores mixture models in discrete choice and random parameters estimation results.

Introduces continuous time Markov chains on a finite state space with exponential waiting times and jump probabilities.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Covers the application of extreme value theory to point processes and the estimation of extreme events from equally-spaced time series.

Covers mathematical tools for communication systems and data science, including information theory and signal processing.

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Covers Markov Chain Monte Carlo examples and error estimation methods.

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.