Modeling Neurobiological Signals: Markov Chains

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Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Explores exponential families, Bernoulli distributions, parameter estimation, and maximum entropy distributions in statistical modeling.

Covers the basics of Markov chains and their algorithmic applications.

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

Covers computation and estimation in stochastic simulation, focusing on generating iid replicas and optimal importance sampling.

Introduces Maximum Likelihood Estimation in econometrics, covering principles, properties, applications, and specification tests.

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

Covers various methods for estimating model parameters, such as method of moments and maximum likelihood estimation.

Explores linear estimation and prediction in AR parametric models, focusing on Yule Walker equations and Wiener filter.