Let's say we want to figure out how fast a particular man walks. Knowing nothing about his ability, initially we say he walks at 1 meters per second. We then measure his speed one day and it comes out 1.3 m/s. According to Bayes, this updates to (1+1.3)/2 = 1.15. If we measure him again at some later date and get 1.3 again it will come to (1.15)*2/3 +1.3/3=1.2.
So the ony real difference between Bayes and some other averaging process is the a priori estimates put on the numbers.
To use Bayes effectivly, you have to find some metric that you have some good prior knowledge about. For the walker, I would have been better off simply measuring how fast the man waks and then kept up a moving average. For those two measurements, I would have gotten 1.3 m/s instead of the Bayesian estimate of 1.2.
Of course there is value in doing all this stuff, as we use this concept every day of our lives in our own personal decision making.
I would suggest instead to apply Bayes theorem to the probability of making new crude oil discoveries. Lots of a prior knowledge built up with new data coming in every year which allows us to update the estimates. I don't know, for some reason, this approach seems a bit more, let me see, practical, than the wankery discussed so far?
And if I have got the particulars of Bayes wrong, well just shoot me, because I am a practical kind of guy.





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