**H**ere are the slides of my tutorial at O’ Bayes 2013 today, a pot-pourri of various, recent and less recent, criticisms (with, albeit less than usual, a certain proportion of recycled slides):

## Archive for slides

## on alternative perspectives and solutions on Bayesian tests

Posted in Statistics, Travel, University life with tags American Journal of Preventive Medicine, Bayesian tests, Duke University, Jeffreys-Lindley paradox, objective Bayes, slides, tutorial, uniformly most powerful tests on December 16, 2013 by xi'an## seminars at CMU and University of Toronto

Posted in Statistics, Travel, University life with tags ABC model choice, Asian beetle, Canada, Carnegie Mellon University, CMU, DIYABC, Ontario, Pennsylvania, Pennsylvn, seminar, slides, slideshare, University of Toronto on October 29, 2013 by xi'an**H**ere are the slides for my seminar talks at Carnegie Mellon University (Pittsburgh) and the University of Toronto, tomorrow and the day after, respectively:

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## snapshot from my SIOD 2013 talk

Posted in pictures, Statistics, Travel, University life with tags ABC, conference, Ho Chi Min City, SIOD 2013, slides, Vietnam on July 11, 2013 by xi'an### Share:

## reading classics (#11)

Posted in Books, Statistics, University life with tags Annals of Mathematical Statistics, classics, Isaac Azimov, John Tukey, slides, students, UMP tests, Université Paris Dauphine on March 21, 2013 by xi'an**T**oday was my last Reading Seminar class and the concluding paper chosen by the student was Tukey’s “The future of data analysis“, a 1962 Annals of Math. Stat. paper. Unfortunately, reading this paper required much more maturity and background than the student could afford, which is the reason why this last presentation is not posted on this page… Given the global and a-theoretical perspective of the paper, it was quite difficult to interpret without further delving into Tukey’s work and without a proper knowledge of what was Data Analysis in the 1960′s. *(The love affair of French statisticians with data analysis was then at its apex, but it has very much receded since then!)* Being myself unfamiliar with this paper, and judging mostly from the sentences pasted by the student in his slides, I cannot tell how much of the paper is truly visionary and how much is cheap talk: focussing on trimmed and winsorized means does not sound like offering a very wide scope for data analysis… I liked the quote “It’s easier to carry a slide rule than a desk computer, to say nothing of a large computer”! (As well as the quote from Azimov “*The sound of panting*“…. (Still, I am unsure I will keep the paper within the list next year!)

**O**verall, despite a rather disappointing lower tail of the distribution of the talks, I am very happy with the way the seminar proceeded this year and the efforts produced by the students to assimilate the papers, the necessary presentation skills including building a background in LaTeX and Beamer for most students. I thus think almost all students will pass this course and do hope those skills will be profitable for their future studies…

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## reading classics (#10 and #10bis)

Posted in Books, Statistics, University life with tags Bayes factors, classics, Jeffreys-Lindley paradox, Jim Berger, p-values, slides, students, testing of hypotheses, Tom Sellke, Université Paris Dauphine on February 28, 2013 by xi'an**T**oday’s classics seminar was rather special as two students were scheduled to talk. It was even more special as both students had picked (without informing me) the very same article by Berger and Sellke (1987), *Testing a point-null hypothesis: the irreconcilability of p-values and evidence*, on the (deep?) discrepancies between frequentist p-values and Bayesian posterior probabilities. In connection with the Lindley-Jeffreys paradox. Here are Amira Mziou’s slides:

and Jiahuan Li’s slides:

for comparison.

**I**t was a good exercise to listen to both talks, seeing two perspectives on the same paper, and I hope the students in the class got the idea(s) behind the paper. As you can see, there were obviously repetitions between the talks, including the presentation of the lower bounds for all classes considered by Jim Berger and Tom Sellke, and the overall motivation for the comparison. Maybe as a consequence of my criticisms on the previous talk, both Amira and Jiahuan put some stress on the definitions to formally define the background of the paper. (I love the poetic line: *“To prevent having a non-Bayesian reality”*, although I am not sure what Amira meant by this…)

**I** like the connection made therein with the Lindley-Jeffreys paradox since this is the core idea behind the paper. And because I am currently writing a note about the paradox. Obviously, it was hard for the students to take a more remote stand on the reason for the comparison, from questioning .the relevance of testing point null hypotheses and of comparing the numerical values of a *p*-value with a posterior probability, to expecting asymptotic agreement between a *p*-value and a Bayes factor when both are convergent quantities, to setting the same weight on both hypotheses, to the *ad-hocquery* of using a drift on one to equate the *p*-value with the Bayes factor, to use specific priors like Jeffreys’s (which has the nice feature that it corresponds to *g=n* in the *g*-prior, as discussed in the new edition of *Bayesian Core*). The students also failed to remark on the fact that the developments were only for real parameters, as the phenomenon (that the lower bound on the posterior probabilities is larger than the *p*-value) does not happen so universally in larger dimensions. I would have expected more discussion from the ground, but we still got good questions and comments on a) why 0.05 matters and b) why comparing *p*-values and posterior probabilities is relevant. The next paper to be discussed will be Tukey’s piece on the future of statistics.