[SciPy-User] [OT] Bayesian vs. frequentist
Wed Feb 15 02:28:15 CST 2012
You're not wrong. I'm in a field that has (partially) embraced Bayesian methods, and one of the challenges in getting it adopted and used in general practice is that it is *much* harder to implement at times. It's not just a matter of the code itself - a tremendous amount of work has to go into obtaining the priors in order for them to be at all meaningful.
You might want to consider popping over to Stack Overflow's sister site, Cross Validated (http://stats.stackexchange.com). I've found it to be an extremely helpful resource for asking questions both on a theory level and a practical applications/coding level.
On Feb 15, 2012, at 3:17 AM, <firstname.lastname@example.org>
> Message: 5
> Date: Wed, 15 Feb 2012 09:21:10 +0100
> From: Daniele Nicolodi <email@example.com>
> Subject: Re: [SciPy-User] [OT] Bayesian vs. frequentist
> To: firstname.lastname@example.org
> Message-ID: <4F3B6AF6.email@example.com>
> Content-Type: text/plain; charset=ISO-8859-1
> 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,
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