Bayesian Inference: Overview
This video introduces Bayesian inference and statistics, which is a powerful framework for learning distributions from data. This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company %%% CHAPTERS %%% 00:00 Intro 01:09 Big Idea: Bayesian Inference 06:04 BI Balances Data and Prior Beliefs 09:43 BI Yields Distribution for Theta 11:01 The Bayesian Update 14:29 Code Demo: Bayesian Hypothesis Testing 23:21 Disadvantages of BI 27:53 Outro
▶︎
Bayesian Updates and Conjugate Priors
▶︎
Maximum Likelihood Estimation (MLE) with Examples
▶︎
The Bayesian Trap
▶︎
Density Estimation with Gaussian Mixture Models (GMM) and Empirical Priors
▶︎
Bayesian Maximum Aposteriori Estimation (MAP): Extending Maximum Likelihood Estimation
▶︎
Explaining the biggest “beef” in statistics | Bayesian #2
▶︎
Introduction to Bayesian Statistics - A Beginner's Guide
▶︎
Monte Carlo Sampling and Bootstrapping in Bayesian Inference
▶︎
Parameter Estimation and Fitting Distributions
▶︎
Reinventing Entropy | Compression is Intelligence Part 1
▶︎
Chip design from the bottom up – Reiner Pope
▶︎
The Chi-Squared Test : Are Two Distributions the Same? (with Python Example)
▶︎
17. Bayesian Statistics
▶︎
The power of Bayesian reasoning | BBC Ideas
▶︎
Introduction to Statistics and Data Analysis
▶︎
Maximum Likelihood Estimation Example: Fitting a Normal Distribution with Data
▶︎
The better way to do statistics | Bayesian #1
▶︎
A visual guide to Bayesian thinking
▶︎