Implicit Generative Models

Related lectures (30)

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Covers the estimation of distributions using various methods such as minimum loss and expectation.

Explores moment-generating functions, linear combinations, and normality of random variables.

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.

Covers probability distributions, moments, and continuous random variables.

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Covers nonparametric statistics, kernel density estimation, Bayesian principles, and posterior distribution summarization.

Explores statistical parameter estimation, sample accuracy, and Bernoulli variables' properties.

Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.