Estimation Methods: BLP and Control-Function

Related lectures (30)

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Covers decoding methods and training challenges in natural language generation.

Explores sampling in maximum likelihood estimation and its implications on the joint probability and likelihood contribution.

Covers Conditional Maximum Likelihood estimation, contribution to likelihood, and MEV model application in choice-based samples.

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

Explores research process, variable types, causality vs correlation, and sampling strategies.

Introduces multivariate statistics, covering normal distribution properties and characteristic functions.

Introduces BlinkDB, a framework for approximate query processing using sampling techniques.

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Introduces the Boltzmann Machine, covering expectation consistency, data clustering, and probability distribution functions.