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Archive of posts filed under the Bayesian Statistics category.

Comparing Waic (or loo, or any other predictive error measure)

Ed Green writes: I have fitted 5 models in Stan and computed WAIC and its standard error for each. The standard errors are all roughly the same (all between 209 and 213). If WAIC_1 is within one standard error (of WAIC_1) of WAIC_2, is it fair to say that WAIC is inconclusive? My reply: No, […]

Stan PK/PD Tutorial at the American Conference on Pharmacometrics, 8 Oct 2015

Bill Gillespie, of Metrum, is giving a tutorial next week at ACoP: Getting Started with Bayesian PK/PD Modeling Using Stan: Practical use of Stan and R for PK/PD applications Thursday 8 October 2015, 8 AM — 5 PM, Crystal City, VA This is super cool for us, because Bill’s not one of our core developers […]

Solution to Stan Puzzle 1: Inferring Ability from Streaks

If you missed it the first time around, here’s a link to: Stan Puzzle 1: Inferring Ability from Streaks First, a hat-tip to Mike, who posted the correct answer as a comment. So as not to spoil the surprise for everyone else, Michael Betancourt (different Mike), emailed me the answer right away (as he always […]

How to use lasso etc. in political science?

Tom Swartz writes: I am a graduate student at Oxford with a background in economics and on the side am teaching myself more statistics and machine learning. I’ve been following your blog for some time and recently came across this post on lasso. In particular, the more I read about the machine learning community, the […]

Fitting models with discrete parameters in Stan

This book, “Bayesian Cognitive Modeling: A Practical Course,” by Michael Lee and E. J. Wagenmakers, has a bunch of examples of Stan models with discrete parameters—mixture models of various sorts—with Stan code written by Martin Smira! It’s a good complement to the Finite Mixtures chapter in the Stan manual.

Stan Puzzle #1: Inferring Ability from Streaks

Inspired by X’s blog’s Le Monde puzzle entries, I have a little Stan coding puzzle for everyone (though you can solve the probabilty part of the coding problem without actually knowing Stan). This almost (heavy emphasis on “almost” there) makes me wish I was writing exams. Puzzle #1: Inferring Ability from Streaks Suppose a player […]

PK/PD Talk with Stan — Thu 8 Oct, 10:30 AM at Columbia: Improved confidence intervals and p-values by sampling from the normalized likelihood

Sebastian Ueckert and France Mentré are swinging by to visit the Stan team at Columbia and Sebastian’s presenting the following talk, to which everyone is invited. Improved confidence intervals and p-values by sampling from the normalized likelihood Sebastian Ueckert (1,2), Marie-Karelle Riviere (1), France Mentré (1) (1) IAME, UMR 1137, INSERM and University Paris Diderot, […]

Have weak data. But need to make decision. What to do?

Vlad Malik writes: I just re-read your article “Of Beauty, Sex and Power”. In my line of work (online analytics), low power is a recurring, existential problem. Do we act on this data or not? If not, why are we even in this business? That’s our daily struggle. Low power seems to create a sort […]

I’m speaking in Germany today!

Right between Mittagspause and Tagungsabschluss, just how I like it. It’s a methods conference for the German Psychological Society in Jena. Here’s my title and abstract: Applied Bayesian Statistics Bayesian methods allow the smooth combination of information from multiple sources and are associated with open acknowledgement about uncertainty. We discuss modern applied Bayesian perspectives on […]

Meet Teletherm, the hot new climate change statistic!

Peter Dodds, Lewis Mitchell, Andrew Reagan, and Christopher Danforth write: We introduce, formalize, and explore what we believe are fundamental climatological and seasonal markers: the Summer and Winter Teletherm—the on-average hottest and coldest days of the year. We measure the Teletherms using 25 and 50 year averaging windows for 1218 stations in the contiguous United […]

Matlab/Octave and Python demos for BDA3

My Bayesian Data Analysis course at Aalto University started today with a record number of 84 registered students! In my course I have used some Matlab/Octave demos for several years. This summer Tuomas Sivula translated most of them to Python and Python notebook. Both Matlab/Octave and Python demos are now available at Github in hope they […]

P-values and statistical practice

What is a p-value in practice? The p-value is a measure of discrepancy of the fit of a model or “null hypothesis” H to data y. In theory the p-value is a continuous measure of evidence, but in practice it is typically trichotomized approximately into strong evidence, weak evidence, and no evidence (these can also […]

Constructing an informative prior using meta-analysis

Chris Guure writes: I am trying to construct an informative prior by synthesizing or collecting some information from literature (meta-analysis) and then to apply that to a real data set (it is longitudinal data) for over 20 years follow-up. In constructing the prior using the meta-analysis data, the issue of publication bias came up. I […]

Performing design calculations (type M and type S errors) on a routine basis?

Somebody writes writes: I am conducting a survival analysis (median follow up ~10 years) of subjects who enrolled on a prospective, non-randomized clinical trial for newly diagnosed multiple myeloma. The data were originally collected for research purposes and specifically to determine PFS and OS of the investigational regimen versus historic controls. The trial has been […]

“Can you change your Bayesian prior?”

Deborah Mayo writes: I’m very curious as to how you would answer this for subjective Bayesians, at least. I found this section of my book showed various positions, not in agreement. I responded on her blog: As we discuss in BDA and elsewhere, one can think of one’s statistical model, at any point in time, […]

Data-analysis assignments for BDA class?

In my Bayesian data analysis class this fall, I’m planning on doing some lecturing and class discussion, but the core of the course will be weekly data-analysis assignments where they do applied statistics using Stan (to fit models) and R (to pre-process the data and post-process the inferences). So, I need a bunch of examples. […]

Macartan Humphreys on the Worm Wars

My Columbia political science colleague shares “What Has Been Learned from the Deworming Replications: A Nonpartisan View”: Last month there was another battle in a dispute between economists and epidemiologists over the merits of mass deworming.1 In brief, economists claim there is clear evidence that cheap deworming interventions have large effects on welfare via increased […]

My 2 classes this fall

Stat 6103, Bayesian Data Analysis Modern Bayesian methods offer an amazing toolbox for solving science and engineering problems. We will go through the book Bayesian Data Analysis and do applied statistical modeling using Stan, using R (or Python or Julia if you prefer) to preprocess the data and postprocess the analysis. We will also discuss […]

ShinyStan v2.0.0

For those of you not familiar with ShinyStan, it is a graphical user interface for exploring Stan models (and more generally MCMC output from any software). For context, here’s the post on this blog first introducing ShinyStan (formerly shinyStan) from earlier this year. ShinyStan v2.0.0 released ShinyStan v2.0.0 is now available on CRAN. This is […]

Fitting a multilevel model

Cui Yang writes: I have a question about the use of BRT (Boosting regression tree). I am planning to write an article about the effects of soil fauna and understory fine roots on forest soil organic carbon. The experiment was conducted in a subtropical forest area in China. There were 16 blocks each with 5 […]