September 2nd, 2014
In Technology
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If you enjoy this article, see the other most popular articles
If you enjoy this article, see the other most popular articles
If you enjoy this article, see the other most popular articles
Bayesian calculations often depend on sampling methods such as Markov Chain Monte Carlo?
(written by lawrence krubner, however indented passages are often quotes). You can contact lawrence at: lawrence@krubner.com, or follow me on Twitter.
I really wish I understood this article. I need to commit to spending some serious time studying statistics, so I can catch up with the modern boom in data analysis. Because 90% of this article is over my head. But from what I can glean, it is very informative:
You’ll noticed that I glossed over something here: the prior, P(Ftrue). The prior allows inclusion of other information into the computation, which becomes very useful in cases where multiple measurement strategies are being combined to constrain a single model (as is the case in, e.g. cosmological parameter estimation). The necessity to specify a prior, however, is one of the more controversial pieces of Bayesian analysis.
A frequentist will point out that the prior is problematic when no true prior information is available. Though it might seem straightforward to use a noninformative prior like the flat prior mentioned above, there are some surprisingly subtleties involved. It turns out that in many situations, a truly noninformative prior does not exist! Frequentists point out that the subjective choice of a prior which necessarily biases your result has no place in statistical data analysis.
A Bayesian would counter that frequentism doesn’t solve this problem, but simply skirts the question. Frequentism can often be viewed as simply a special case of the Bayesian approach for some (implicit) choice of the prior: a Bayesian would say that it’s better to make this implicit choice explicit, even if the choice might include some subjectivity.
Post external references
- 1
http://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/
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