Chapter 3: Using All the Information: Estimating Parameters and Bayes' Theorem
Ok, here it is the last chapter on the basics. Bayes' Theorem is quite advanced actually, but it is what we will need later for exploitative play. After just a few trials, samples regress to the mean so quickly that you can use this against your opponent quickly. Gathering tendencies of your opponents and being certain of them to a degree will make you play optimally against them.
Anyway, here are a few topics to discuss:
1) I hope you have read or heard about the Monty Hall problem. That is the holy grail to understanding Bayes' theorem. If you don't know about it, read about it, understand it!
2) I found a kind of interesting article on Bolt's supposed steroid use on 2+2 invoking Bayes' theorem. I thought it was interesting application (Scientific American article).
Anyway, if something is way off the mean, first start doubting the test that assigns that value, then the value itself. Do you think that could be a nice summary of Bayes' applicable to poker?
Oh, and week 3 assignments are here.
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1 comment:
1) It's a fun non-intuitive problem that can be tested empiricially and people will still think you're doing something wrong.
2) I'm not impressed by the application of Bayes' Theorem as we have no a priori knowledge of the distributions. It would be better to have a statistical distribution to know just how far away Bolt's height is from the mean (within how many standard deviations)? It's also hard because you have to take a population sample from among comparable sprinters (ie, Olympic-quality) and not just the average population.
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