[unable to retrieve full-text content]Judea Pearl has made noteworthy contributions to artificial intelligence, Bayesian networks, and causal analysis. These achievements notwithstanding, Pearl holds some views many statisticians may find odd or exaggerated.
Original Post: The Book of Why
[unable to retrieve full-text content]A Turing Prize-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize AI.
Original Post: THE BOOK OF WHY: The New Science of Cause and Effect
[unable to retrieve full-text content]BNNs are important in specific settings, especially when we care about uncertainty very much.
Original Post: What is a Bayesian Neural Network?
[unable to retrieve full-text content]Also: New Poll: Data Science / Machine Learning methods you used; The amazing predictive power of conditional probability in Bayes Nets; The 10 Statistical Techniques Data Scientists Need to Master.
Original Post: Top KDnuggets tweets, Nov 15-21: DeepLearning is “shallow”: here are underlying concepts you need
[unable to retrieve full-text content]This article explains how Bayes Nets gain remarkable predictive power by their use of conditional probability. This adds to several other salient strengths, making them a preeminent method for prediction and understanding variables’ effects.
Original Post: The amazing predictive power of conditional probability in Bayes Nets
[unable to retrieve full-text content]Bayes Nets have remarkable properties that make them better than many traditional methods in determining variables’ effects. This article explains the principle advantages.
Original Post: How Bayesian Networks Are Superior in Understanding Effects of Variables