Pascal Bercker·2d agoPrecise Bayesian Conditioning: STEP Probability Meets Netica“Given that” is the most dangerous phrase in probability. — Keith McNulty
Michael Leznik·2d agoForty-Six Thousand VarietiesThe Bayesians could not agree either — and what your 97% means to each of them
Sudani Priyavasanthi·Nov 2Naive Bayes Algorithm: Understanding Naive Bayes in Machine LearningMachine Learning helps computers learn patterns from data and make predictions. One of the simplest and most effective algorithms used for…
V V Srimannarayana Reddy Tetali·Nov 2How Naive Bayes Actually Works: Understanding the Algorithm Behind the PredictionsIntroduction to Naive Bayes
InMath GamesbyPascal Bercker·Oct 29Stop Me When the Black Marbles Are Gone!From a fixed hypergeometric draw to a surprisingly scale-invariant probability puzzle.
sandeep rawat·Oct 27Your Test Came Back Positive. Are You Really Sick? A simple introduction to Bayes’ Theorem1. The story
InData Science CollectivebyAbhisake Jain·Oct 15The Test Is 99% Accurate. So Why Am I Probably Healthy?You test positive for a rare disease. The test is 99% accurate. Your chance of being sick is only about 9%. Let’s find out why.A response icon1A response icon1
Max Kleiner·Sep 29From Bayes Theorem to Confusion MatrixA confusion matrix supplies empirical counts of errors, while Bayes’ theorem explains how those error rates and the class prevalence…A response icon2A response icon2
Samah Abdelrahim·Sep 9Abraham, fire, and Bayesian inferenceIn the books, and by books I mean arguably divine books like the Quran and post-biblical interpretations, a story about the…
Patrick Lefler·Sep 1The Prior Problem: Why Risk Reporting Needs Bayes’ TheoremArticle 1 of 8 | The Bayesian Risk Series
