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February 13th, 2013, 03:23 AM  #1 
Newbie Joined: May 2012 Posts: 2 Thanks: 0  Understanding Bayesian statistics, repeated measurements and
I am getting to grips with Bayesian statistics. I understand how to calculate the maximum a posteriori probability (MAP) estimate for the mean, given the sample data and the a priori mean and standard deviation. However, for my own understanding of Bayesain statistics, i would also like to know how to answer the following question  in a Bayesian paradigm: The coach of the local american footbal team has a hunch that the field is less than 100 yrds. He also has an old yardstick, which he uses to measure the field, obtaining n=3 datapoints D={101, 100.5, 101.5} Question: After each measurement, how should he update his belief about the field length? Specifically, how to recalculate the probability the field is less then 100 yrds after each measurement? I know i should start with replacing the 'hunch' with a probability that the field is less than 100 yrds. Then, i assume, i should also assign some measure of uncertainty to my measurements, but i dont know how. Example taken from this very informative video lecture, series of lectures, really: http://www.youtube.com/watch?v=0LQmZXCWMFI Thanks, Ivana 

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bayesian, measurements, repeated, statistics, understanding 
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