Gaussian Denoising: Statistical Physics Connection

Related lectures (32)

Page 3 of 4

Covers the Curie-Weiss model in Statistical Physics, including magnetization probability, free entropy, and the cavity method.

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Covers the likelihood ratio test for detection and estimation in statistical analysis.

Explores scaling and renormalization in statistical mechanics, emphasizing critical points and invariant properties.

Covers demousing, estimation, Bayesian inference, likelihood, AWGN, and more.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Explores the definition and derivability of functions in differential calculus, emphasizing differentiability at specific points.

Covers hypothesis testing, p-values, significance levels, and Bayesian estimation.

Discusses Bayesian inference for the mean of a Gaussian distribution with known variance, covering posterior mean, variance, and MAP estimator.