Parameter

Related lectures (32)

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

Covers the concept of point estimation in statistics, focusing on methods to estimate unknown parameters from a given sample.

Explores sub exponential bounds and Bernstein conditions in verifying sub exponentiality.

Covers variational inference and neural networks for classification tasks.

Covers the analysis of calibrations for flow meters and parameters like atmospheric temperature, pressure, humidity, and timing.

Explores probability distributions, parameters, and transformations to analyze events within distributions.

Explores modeling neurobiological signals with Markov Chains, focusing on parameter estimation and data classification.

Covers linear regression, gradient descent, overfitting, and ridge regression among other concepts.

Covers the concept of distribution estimation and the optimization of parameters using different estimators.

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

Explores causal inference, emphasizing the importance of committing to an ontology for drawing causal inferences and selecting appropriate estimands.

Covers the concept of the exponential family and discusses forward and backward maps, expensive computations, parameters, functions, and convexity.

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