A visual introduction to probability and statistics, covering basic probability, compound probability, probability distributions, frequentist inference, Bayesian inference, and regression analysis. Created by Daniel Kunin and team with interactive visualizations using D3.js.
This guide walks through applications, libraries, and dependencies of causal discovery approaches using Bayesian modeling, with a step-by-step guide on creating causal networks using discrete or continuous datasets, explaining techniques and search methods like PC and Hill Climb Search, ensuring readers understand Bayesian techniques for causal discovery in specific use cases."
