Category Archives: Conferences

Belief-Sustaining Inference

TL;DR: If you’re in Pittsburgh today, come to SIGBOVIK 2014 at CMU at 5pm for free food and incredible math!

In a recent chat with my classmate Alex Reinhart, author of Statistics Done Wrong, we noticed a major gap in statistical inference philosophies. Roughly speaking, Bayesian statisticians begin with a prior and a likelihood, while Frequentist statisticians use the likelihood alone. Obviously, there is scope for a philosophy based on the prior alone.

We began to develop this idea, calling it Belief-Sustaining Inference, or BS for short. We discovered that BS inference is extremely efficient, for instance getting by with smaller sample sizes and producing tighter confidence intervals than other inference philosophies.

Today I am proud dismayed complacent to report that our resulting publication has been accepted to the prestigious adequate SIGBOVIK 2014 conference (for topics such as Inept Expert Systems, Artificial Stupidity, and Perplexity Theory):

Reinhart, A. and Wieczorek, J. “Belief-Sustaining Inference.” SIGBOVIK Proceedings, Pittsburgh, PA: Association for Computational Heresy, pp. 77-81, 2014. (pdf)

Our abstract:

Two major paradigms dominate modern statistics: frequentist inference, which uses a likelihood function to objectively draw inferences about the data; and Bayesian methods, which combine the likelihood function with a prior distribution representing the user’s personal beliefs. Besides myriad philosophical disputes, neither method accurately describes how ordinary humans make inferences about data. Personal beliefs clearly color decision-making, contrary to the prescription of frequentism, but many closely-held beliefs do not meet the strict coherence requirements of Bayesian inference. To remedy this problem, we propose belief-sustaining (BS) inference, which makes no use of the data whatsoever, in order to satisfy what we call “the principle of least embarrassment.” This is a much more accurate description of human behavior. We believe this method should replace Bayesian and frequentist inference for economic and public health reasons.

If you’re around CMU today (April 1st), please do stop by SIGBOVIK at 5pm, in Rashid Auditorium in the Gates-Hillman Center. There will be free food, and that’s no joke.

Carl Morris Symposium on Large-Scale Data Inference (3/3)

The final summary of last week’s symposium on statistics and data visualization (see part 1 and part 2)… Below I summarize Chris Volinsky’s talk on city planning with mobile data, and the final panel discussion between the speakers plus additional guests.

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Carl Morris Symposium on Large-Scale Data Inference (2/3)

Continuing the summary of last week’s symposium on statistics and data visualization (see part 1 and part 3)… Here I describe Dianne Cook’s discussion of visual inference, and Rob Kass’ talk on statistics in cognitive neuroscience.

[Edit: I’ve added a few more related links throughout the post.]

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Carl Morris Symposium on Large-Scale Data Inference (1/3)

I enjoyed this week’s Symposium on Large-Scale Data Inference, which honored Harvard’s Carl Morris as the keynote speaker. This was the 2nd such symposium; last year’s honoree was Brad Efron (whose new book I also recommend after seeing it at this event).

This year’s focus was the intersection of statistics and data visualization around the question, “Can we believe what we see?” I was seriously impressed by the variety and quality of the speakers & panelists — many thanks to Social & Scientific Systems for organizing! Look for the lecture videos to be posted online in January.

See below for the first two speakers, Carl Morris and Mark Hansen. The next posts will summarize talks by Di Cook and Rob Kass (part 2), and Chris Volinsky and the final panel discussion (part 3).

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Making R graphics legible in presentation slides

I only visited a few JSM sessions today, as I’ve been focused on preparing for my own talk tomorrow morning. However, I went to several talks in a row which all had a common problem that made me cringe: graphics where the fonts (titles, axes, labels) are too small to read.

You used R's default settings when putting this graph in your slides? Too bad I won't be able to read it from anywhere but the front of the room.

Dear colleagues: if we’re going to the effort of analyzing our data carefully, and creating a lovely graph in R or otherwise to convey our results in a slideshow, let’s PLEASE save our graphs in a way that the text is legible on the slides! If the audience has to strain to read your graphics, it’s no easier to digest than a slide with dense equations or massive tables of numbers.

For those of us working in R, here are some very quick suggestions that would help me focus on the content of your graphics, not on how hard I’m squinting to read them.

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JSM 2012: Sunday

Greetings from lovely San Diego, CA, site of this year’s Joint Statistical Meetings. I can’t believe it’s already been a year since I was inspired to start blogging during the JSM in Miami!

If you’re keeping tabs on this year’s conference, there’s a fair amount of #JSM2012 activity on Twitter. Sadly, I haven’t seen any recent posts on The Statistics Forum, which blogged JSM so actively last year.

Yesterday’s Dilbert cartoon was also particularly fitting for the start of JSM, with its focus on big data :)

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useR 2012: main conference braindump

I knew R was versatile, but DANG, people do a lot with it:

> > … I don’t think anyone actually believes that R is designed to make *everyone* happy. For me, R does about 99% of the things I need to do, but sadly, when I need to order a pizza, I still have to pick up the telephone. —Roger Peng

> There are several chains of pizzerias in the U.S. that provide for Internet-based ordering (e.g. so, with the Internet modules in R, it’s only a matter of time before you will have a pizza-ordering function available. —Doug Bates

Indeed, the GraphApp toolkit … provides one (for use in Sydney, Australia, we presume as that is where the GraphApp author hails from). —Brian Ripley

So, heads up: the following post is super long, given how much R was covered at the conference. Much of this is a “notes-to-self” braindump of topics I’d like to follow up with further. I’m writing up the invited talks, the presentation and poster sessions, and a few other notes. The conference program has links to all the abstracts, and the main website should collect most of the slides eventually.

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useR 2012: impressions, tutorials

First of all, useR 2012 (the 8th International R User Conference) was, hands down, the best-organized conference I’ve had the luck to attend. The session chairs kept everything moving on time, tactfully but sternly; the catering was delicious and varied; and Vanderbilt University’s leafy green campus and comfortable facilities were an excellent setting. Many thanks to Frank Harrell and the rest of Vanderbilt’s biostatistics department for hosting!

Plus there's a giant statue of bacon. What's not to love?

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useR 2012: my materials

Just a quick note that I’ve posted the slides, code, and dataset from my useR 2012 talk.

I’m having a great time here in Nashville and will write up a conference review soon, with links to the many excellent packages and resources I’ve been discovering.

JSM: accessible for first-year grad students?

A friend of mine has just finished his first year of a biostatistics program. I’m encouraging him to attend the Joint Statistical Meetings (JSM) conference in San Diego this July/August. He asked:

Some of the talks look really interesting, though as a someone who’s only been through the first year of a master’s program, I wonder if I’d be able to understand much.  When you went as a student, did you find the presentations to be accessible?

I admit a lot of the talks went over my head the first year — and many still do. Some talks are too specialized even for an experienced statistician who just has a different focus… But there are always plenty of accessible talks as well:

  • Talks on teaching statistical literacy or Stats 101 might be useful if you’re ever a TA or consultant
  • Talks on data visualization may focus on communicating results rather than on technical details
  • Overview lectures can introduce you to a new field
  • Some folks are known for generally being accessible speakers (a few off the top of my head: Hadley Wickham, Persi Diaconis, Andrew Gelman, Don Rubin, Dick DeVeaux, David Cox, Hal Varian… and plenty of others)

And it’s worthwhile for a grad student to start getting to know other statisticians and becoming immersed in your field.

  • There’s a nice opening night event for first-time attendees, and the Stat Bowl contest for grad students; in both of those, I made some friends I keep seeing again at later JSMs
  • Even when the talk is too advanced, it’s still fun to see a lecture by the authors of your textbooks, meet the folks who invented a famous estimator, etc.
  • You can get involved in longer-term projects: after attending the Statistics Without Borders sessions, I’ve become co-chair of the SWB website and co-authored a paper that’s now under review
  • It’s fun to browse the books in the general exhibit hall, get free swag, and see if any exhibitors are hiring; there is also a career placement center although I haven’t used it myself

Even if you’re a grad student or young statistician just learning the ropes, I definitely think it’s worth the trip!