You’ll hear the term Bayes or Bayesian come up a lot in data science, but this video explores the theory with tennis balls and a table.
Here’s a curated list of articles on Bayesian methods and networks.
- An Introduction to Bayesian Reasoning
- Basics of Bayesian Decision Theory
- How Bayesian Inference Works
- Marketing Insight from Unsupervised Bayesian Belief Networks
- Bayesian Nonparametric Models
- Using Bayesian Kalman Filter to predict positions of moving particles
- Naive Bayes Classification explained with Python code
- Wheel Of Fortune – Bayesian Inference
- Neural Networks from a Bayesian Perspective
- A curated list of resources dedicated to bayesian deep learning
- A quick introduction to PyMC3 and Bayesian models
- Analysis of Perishable Products Sales Using Bayesian Inference
- R and Stan: introduction to Bayesian modeling
- And Monty Hall Went Bayesian…
- Bayesian Probability
Here’s a great exploration of Bayes’ Theorem and how to use it in real world problems.
Bayes’ theorem is a way to figure out conditional probability. Conditional probability is the probability of an event happening, given that it has some relationship to one or more other events. For example, your probability of getting a parking space is connected to the time of day you park, where you park, and what conventions are going on at any time. Bayes’ theorem is slightly more nuanced. In a nutshell, it gives you the actual probability of an event given information about tests.
Here’s a great introduction to Bayes Theorem and Hidden Markov Models, with simple examples. If you understand basic probability, then you can follow along.
Julia Galef outlines the most important principles of thinking like a Bayesian.
Julia Galef uses pictures to illustrate the mechanics of “Bayes’ rule,” a mathematical theorem about how to update your beliefs as you encounter new evidence.