Probability

Probability Machine Learning and Pattern Recognition Chris Williams School of Informatics, University of Edinburgh August 2014 (All of the slides in this course have been adapted from previous versions by Charles Sutton, Amos Storkey, David Barber.) 1 / 31 Outline I What is probability? I Random Variables (discrete and continuous) I Expectation I Joint Distributions I Marginal Probability I Conditional Probability I Chain Rule I Bayes' Rule I Independence I Conditional Independence I Some Probability Distributions (for reference) I Reading: Murphy secs 2.1-2.4 2 / 31 What is probability? I Quantication of uncertainty I Frequentist interpretation: long run frequenies of events I Example: The probability of a particular coin landing heads up is 0.43 I Bayesian interpretation: quantify our degrees of belief about something I Example: the probability of it raining tomorrow is 0.3 I Not possible to repeat \tomorrow" many times I Basic rules of probability are the same, no matter which interpretation is adopted 3 / 31