[SciPy-User] [OT] Bayesian vs. frequentist

Lou Pecora lou_boog2000@yahoo....
Wed Feb 15 08:37:41 CST 2012


From: Daniele Nicolodi <daniele@grinta.net>

To: scipy-user@scipy.org 
Sent: Wednesday, February 15, 2012 3:21 AM
Subject: Re: [SciPy-User] [OT] Bayesian vs. frequentist
 
Hello, I'll hijack this thread to ask for advice.

I'm a physicist and, as you may expect, my education in statistics is
mostly in Frequentists methods. However, I always had an interest in
Bayesian methods, as those seems to solve in much more natural ways the
problems that arise in complex data analysis.

I recently started to read "Data Analysis, A Bayesian Tutorial" by D.S.
Silva (currently reading chapter 4, unfortunately real work is always
interfering) and I really like the approach and the straight forward
manner in which the theory builds up.

However, I feel that the Bayesian approach, is much more difficult to
translate to practical methods I can implement, but I may be biased by
the long term exposition to the "recipe based" Frequentist approach.

Can someone suggest me some resources (documentation or code) where some
practical approaches to Bayesian analysis are taught?

Thank you. Cheers,
-- 
Daniele
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I'm also a physicist and just getting into all this.  Silva's book is good.  Here are two others I found that look good and readable.  I have not read either all the way, but they are worth examining.  You should also (after digesting some standard Bayesian statistics) examine the newer latent Dirichlet methods which look pretty powerful and seem to have a better way to handle and generate priors.  Again, I'm a novice here, but these look like good avenues for a scientist trying to learn Bayesian statistics.

(1) Udo von Toussaint, "Bayesian inference in physics", REVIEWS OF MODERN PHYSICS, VOLUME 83, JULY–SEPTEMBER 2011


(2) Daniela Calvetti and Erkki Somersalo, Introduction to Bayesian scientific computing (Springer, 2007)



It's a good topic even if it's OT -- provided everyone remains civil.  :-) 


 
-- Lou Pecora, my views are my own.


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