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Bayesian statistics learning materials

  1. Oct 29, 2012 #1
    Does anyone here know any good Bayesian statistics, Bayesian hypothesis testing, Bayesian inference, etc. learning materials (preferably online)?
     
  2. jcsd
  3. Oct 29, 2012 #2
    We can't say without knowing your background.
     
  4. Oct 29, 2012 #3
    I'm looking for introductory level learning materials. I only know the most essential basics, or even less than that.
     
  5. Oct 30, 2012 #4

    chiro

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    If you are learning Bayesian Inference I'd strongly suggest you become comfortable with standard non-Bayesian Inference first before doing both at the same time.

    Some books cover both in the one book, but I still stand by my recommendation.
     
  6. Oct 30, 2012 #5
    I'm more or less familiar with frequentist inference.
     
  7. Oct 31, 2012 #6
    I know it's not online, but anyway, I strongly recommend this book (any edition, of course):
    Gelman, A., Carlin, J. B., Stern, H. S. Rubin, D. B., Bayesian Data Analysis, Second Edition (Chapman & Hall/CRC Texts in Statistical Science), 2nd edn. (Chapman and Hall/CRC, 2003).

    Its main advantage compared to other famous books (e.g. Robert's The Bayesian Choice or Bernardo&Smith's Bayesian Theory) is its straightforward approach. While the others first develop the decision-theoretic framework and set Bayesian methods within it, Gelman hits directly the "statistical core" of Bayesianism and provides computational means already in the first pages. For a newcomer I find this approach more digestible and better for getting the idea what's Bayesian statistics about.

    Regarding the online sources, I recommend to walk through Christian Robert's blog, where, among others, you can find references to his teaching material. Jim Albert's teaching blog is great too.
     
    Last edited: Oct 31, 2012
  8. Oct 31, 2012 #7
    Thanks a lot for your suggestions, I will order the book in question today.

    By the way, is the Bayesian approach often used in science? Why doesn't it supersede the traditional scientific methodology of "true" or "false" approaches to the empirical hypothesis confirmation and theory credibility, and provide a likelihood framework instead, which could allow more rational decisions to be made?
     
  9. Oct 31, 2012 #8
    Well, the Bayesian approach is already a well established counterpart of the traditional frequentist statistics. The reason why it took so much time (although it's far older then frequentism) consisted mainly in the enormous denial from the traditional Fisher's and Neyman-Pearson's schools. However, their arguments were more or less philosophical and based on comparison of hardly comparable ideas. At this point it is worth to notice that even Fisherians and Neyman-Pearsonians fought each other :-)) If you become interested in the tangled history of Bayesian theory, I suggest reading McGraynes book "The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy", its really worth those $10 on Amazon. I also plan to read Gelman's and Robert's paper dealing with the affair.

    The use of Bayesian methods is now widespread, you can meet them in the computer science, biology, geology, genetics, economy etc. etc. Unlike the traditional methods, they allow to quantify the uncertainty related to statistical decision (e.g. parameter estimation). This, in turn, allows to base decisions even on a very small sample and, if necessary, express the initial informative belief. I (not being a militant advocate of any of the two camps) believe that both Bayesian and frequentist methods are worth knowing and application when they suit circumstances. Both have pros and cons :-)
     
  10. Oct 31, 2012 #9

    Thanks for your book suggestion, I will order that one as well.
    But aren't Bayesian methods far less widespread than the frequentist ones? Most studies I read seem to be using the frequentist approach and frequentist hypothesis testing. Why is there such a preference for the frequentist methods in the scientific community, despite the Bayesian ones providing a far more clear and certain image of the real world?
     
  11. Oct 31, 2012 #10

    chiro

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    It depends.

    In areas where you have small samples (like in medicine and in trials in bio-statistics and health related areas) then the Bayesian approach is a good one since adequate priors give some extra flexibility in dealing with low sample sizes.

    Also the Bayesian approach has well developed theoretical methods like MCMC techniques that allow you to simulate really complex distributions where you have all kinds of crazy dependencies with standard techniques which is good when it comes to simulating processes that you couldn't ordinarily simulate.

    If you have a good reason where some non-standard prior will give results that are more accurate in the context of your work, then this is good thing and I've highlighted one area which has to do with small sample sizes and one with complex models and if these are big issues, then the Bayesian approach will at some point, get a look in.
     
  12. Oct 31, 2012 #11
    In addition to chiro's reply, I'd add that the basic reasons why frequentist methods dominate are (i) historical - they were generally accepted quite recently, (ii) they are much harder to learn and understand for non-mathematicians (non-statisticians), (iii) they do not provide a simple bunch of methods easy to use and (very frequently) misuse. One usually needs to think about what he's doing, not simply feed a software with some (maybe spurious) data and click on a button to get some result (whatever one understands to be a result). Also, as you mentioned, the user doesn't obtain a simple dichotomous answer of type "yes" or "no" as, e.g. in classical hypotheses testing (again, whatever it means).
     
  13. Oct 31, 2012 #12
    Thanks for your response. By saying "yes" or "no" I made an analogy to the traditional hypothesis testing. Because the null hypothesis is either rejected "no", or accepted "yes" depending on the confidence intervals. Instead of having "yes" or "no" answers for given confidence intervals, it would be more convenient to have the probability of the null hypothesis being rejected.
     
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