Hello,
I have a lot of historical data, from the past 20 years, I've been told that Bayesian could increase the predictions of several interesting subjects like the sample size or the model parameters estimation.
I would like to wake up all of these data (I have like hundreds of variables for ~100000 data).
Do you know what good uses of historical data from experiments I could do by the use of Bayesian ?
Thank you very much for your answers.
Yacine HAJJI
Yes, the Bayesian approach to statistical inference is based on the assumption that the parameter can have a probability distribution instead of having a constant value. Knowledge of this prior probability distribution of the parameter may be used to update the distribution when data becomes available to obtain its posterior distribution. This Bayesian approach is known to provide better estimates than the Classical approach for which the parameter is not treated as a random variable. Historical data may be taken advantage of in suggesting a prior distribution for the parameter in question.
Hi,
You could compute the likelihood of the historical data and use it as the prior for the current analysis. In the book of Bayesian Biostatistics, the authors give a number of example of how to turn the historical data into the prior.
http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470018232.html
Thank you all for your answers.
I checked the Biostatistics Bayesian book and just purchased it!
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