Gaussian Denoising: Statistical Physics Connection

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 2 of 4

Bayesian Inference: Optimal Estimation

Explores optimal Bayesian inference, denoising, scalar estimation, and phase transitions.

Sexing Guppy Fish: Bayesian Inference and Decision Rules

Covers the sexing of guppy fish using Bayesian inference and decision rules.

Latent Variable Models

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.

Molecular Dynamics and Monte Carlo

Covers computational methods for molecular systems at finite temperature, emphasizing stochastic sampling and time evolution simulations.

Monte Carlo: Markov Chains

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

Potts Model: Introduction

Introduces the Potts model, a generalization of the Ising model used in statistical mechanics to study phase transitions.

Maximum Likelihood Inference

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

Renormalization Group: Energy Flow

Explores energy flow and critical points in statistical physics, emphasizing the importance of understanding correlation functions and critical exponents.

Phase Equilibria: Understanding Vapor-Liquid Separation

Provides an overview of vapor-liquid phase diagrams and their role in separation processes, focusing on phase equilibria and the Gibbs phase rule.

Linear Regression: Statistical Inference Perspective

Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.