Eliminating Nuisance Parameters: Lemmas in Statistical Inference

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 1 of 4

Eliminating Nuisance Parameters: Statistical Inference

Covers the elimination of nuisance parameters in statistical inference using Lemmas 14 and 15.

Statistical Inference: Model Selection and Nuisance Parameters

Covers model selection, nuisance parameters, and higher-order inference methods in statistical inference.

Statistical Inference: Exponential Families and Likelihoods

Explores exponential families, likelihood functions, and model regularity in statistical inference.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Regular Exponential Family Models

Explores regular exponential family models, unifying distributions like Poisson, binomial, and normal under a common framework.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Sufficient Statistics: Understanding Data Compression

Explores sufficient statistics, data compression, and their role in statistical inference, with examples like Bernoulli Trials and exponential families.

Exponential Family

Covers the properties of the exponential family and the estimation of parameters.

Probabilistic Models for Linear Regression

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

Probability and Statistics: Independence and Conditional Probability

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.