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...Probabilistic machine learning not all models are but most of them have interpretations predictions need to associated confidence probability arguments for approach complete framework makes assumptions explicit recovers non as special cases modular easily extensible foundations algorithms and cs iit kgp indrajit bhattacharya references introduction sheldon ross statistics engineers scientists dimitri p bertsekas john n tsitsiklis basics random experiment outcome events sample space measure axioms basic laws discrete continuous conditional multiplicative rule theorem total bayes independence pair wise mutual variables example tossing two coins variable sum outcomes mass function...