InDev GeniusbyPankaj Chejara, PhD·Aug 23, 2025Conditional Independence Made SimpleConditional independence is a crucial building block of probabilistic graphical models (e.g., Bayesian networks). Having a solid…
Belal Alkufri·Aug 4, 2025How to Imagine the Conditional Independence.firstly, If you can not imagine what is exactly meant with “given” in P(X|Y) which stands for conditional probability, then I recommend you…
Ankur Ankan·Feb 16, 2025Causal Discovery with Mixed Data using pgmpyCausal discovery with mixed data can be tricky because most methods assume everything is purely discrete or purely continuous. As a result…A response icon2A response icon2
InArtificial Intelligence in Plain EnglishbyGiri Rabindranath·Nov 10, 2023Why Do Naive Bayes Classifiers Perform So Well?When dependencies exist, they work together or cancel out
Yu Yang·Feb 5, 2021Understand d-separationD-separation is a critical idea in Bayesian Networks. The problem it intends to tackle is: given a causal graph G, is a set X of variables…A response icon3A response icon3
