Lecture & Exercises for Thu September 26_澳门葡京手机版app下载

nils
2013-09-19T22:56:41.147Z
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www.uio.no

Thu Sept 19 I discussed Bernstein-von Mises theorems, concerning (a) the ML estimator thetahat being approximately N(theta0, Jhatinverse), with theta0 denoting the true parameter value, and (b) its Bayesian mirror statement, that theta given data is approximately N(thetahat, Jhatinverse). Here Jhat is the observed information matrix, minus the Hessian matrix of the log-likelihood function at the ML. I also went through Gott würfelt nicht, Nils Exercises 13, 14, and started discussing simulation strategies, at the start of Ch 3.

For Thu Sept 26 I shall go through Bayes Factors (from Ch 2) and proceed with more simulation techniques. Exercises to go through: Exam stk 4020 from 2012, #1, then #2 (a)-(b)-(c). This latter point involves programming the log-likelihood function, say logL(a,b,c), then using "nlm" of R to find its maximiser and the Hessian matrix. For comments & tricks pertaining to this task (a recurrent one, for the rest of our course), you may e.g. c...