18.05 Lecture Notes - Lecture 6: Mit Opencourseware
Document Summary
Beta try again. (adapted from information theory, infer ence, and learning algorithms" by david j. c. A statistical statement appeared in the guardian on friday january 4, 2002: When spun on edge 250 times, a belgian one-euro coin came up heads 140 times and tails 110. It looks very suspicious to me", said barry blight, a statistics lecturer at the london school of economics. If the coin were unbiased the chance of getting a result as extreme as that would be less than 7%". We will study that in the next unit and return to this problem. Here we will do a bayesian analysis of the data. In class and in the notes we considered the odds and bayes factor for a hypothesis h vs. h c . Given data d we have the posterior odds of h vs. h c are odds = We have is the bayes factor and the factor.
