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BGonline.org Forums
A better approch?
Posted By: Maik Stiebler In Response To: A better approch? (Timothy Chow)
Date: Wednesday, 4 November 2009, at 8:40 p.m.
I still think that for the purposes of conceptual clarity, it is better to compute what you want to compute (or to put it another way, to describe correctly what it is you've computed) than to compute one thing and claim that it is something else,
So far, I totally agree. I'd even start a fracas (learned a new word there, thank you Chuck) and say that the frequentist viewpoint is mostly useless for discussing rollout results.
The funny thing is that I don't think that the thing that started this subthread, Xavier's method for computing the probability that a given move is 'best', warrants an anti-frequentist rant at all. Numerical or Monte Carlo integration is exactly what a Bayesian would do with his posterior distributions to answer the same question. And I claim that the normal (or maybe student, Xavier did not specify) distributions Xavier used are (I retract my previous wording 'happen to be') very reasonable posteriors. If you assume that the trial results for each move are normally distributed with a known variance sigma^2 (brushing over that technicality again for now) and unknown mean mu, and your prior distribution of mu is uniform, the posterior distribution of mu after n trials is the familiar looking (m, sigma^2/n) normal distribution, where m is the sample mean.
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