Bayes statistics and poker
Why is bayesian statistics more applicable to poker than regular statistics?
With regular statistics you make no assumptions, you take a subject (in our case a poker player) and you wait for trials to identiy the subject. You need a lot of trials to deduce anything, as until many trials occur, your data will be statistically insignificant.
With Bayes statistics you take a subject make your best guess as to what the subject is, and then modify the subject with every trial.
For example: a player sits down and we GUESS that it is 10% likely that he is a maniac who will raise 80% of his hands from the cut-off, or he is 90% likely to be a tight player who will raise 10% of his hands rom the cut-off.
The first hand he is dealt he is sitting in the cut-off and raises.
In classical statistics we would make no assumptions like he is tight or loose because we would be guessing. We must wait for trials. When he raises once it is statistically insignificant, and should not be acted on.
With bayes statistics the above information is immediately relavent because we made guesses about the player, and will now begin modifiing our guesses with every single bit of new information.
Duh. you might be saying..
when the player above raises what is the likely hood that he is a maniac?
This is an interesting question, and hope to get alot of answers. Like another recent post it points to intuitive poker play, by exposing the logic and data sets folks work with. This also illuminates why some believe they are terminally unlucky. ALL the logic in their mind tells
them, well I was a favorite here and there etc., but the very logic being applied is flawed.