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Archive of posts filed under the Bayesian Statistics category.

Actually, I’d just do full Bayes

Dave Clark writes: I was hoping for your opinion on a topic related to hierarchical models. I am an actuary and have generally worked with the concept of hierarchical models in the context of credibility theory. The text by Bühlmann and Gisler (A Course in Credibility Theory; Springer) sets up the mixed models under the […]

I definitely wouldn’t frame it as “To determine if the time series has a change-point or not.” The time series, whatever it is, has a change point at every time. The question might be, “Is a change point necessary to model these data?” That’s a question I could get behind.

From the Stan users list: I’m trying out to fit a time series that can have 0 or 1 change point using the sample model from Ch 11.2 of the manual. To determine if the time series has a change-point or not, would I need to do comparison model (via loo) between 1-change model developed […]

Stan Case Studies Launches

There’s a new section of the Stan web site, with case studies meant to illustrate statistical methodologies, classes of models, application areas, statistical computation, and Stan programming. Stan Case Studies The first ten or so are up, including a grab bag of education models from Daniel Furr at U.C. Berkeley: Hierarchical Two-Parameter Logistic Item Response […]

Swimsuit special: “A pure Bayesian or pure non-Bayesian is not forever doomed to use out-of-date methods, but at any given time the purist will be missing some of the most effective current techniques.”

Joshua Vogelstein points me to this paper by Gerd Gigerenzer and Julian Marewski, who write: The idol of a universal method for scientific inference has been worshipped since the “inference revolution” of the 1950s. Because no such method has ever been found, surrogates have been created, most notably the quest for significant p values. This […]

Lack of free lunch again rears ugly head

We had some discussion on blog the other day of prior distributions in settings such as small experiments where available data do not give a strong inference on their own, and commenter Rahul wrote: In real settings I rarely see experts agree anywhere close to a consensus about the prior. Estimates are all over the […]

How should statisticians and economists think about recreational gambling?

Recreational gambling is a lot like recreational drinking, in that it is pleasant, and it can be abused, and the very aspects that make it pleasant are related to what makes it so destructive when abused. Also, both industries make a lot of money, so there’s a continuing tug of war between those who sell […]

The unbelievable reason that Jennifer Lawrence is using Waic and cross-validation for survival models

Sam Brilleman writes: I’ve been reading two of your recent papers: (1) Gelman A, Hwang J, Vehtari A. Understanding predictive information criteria for Bayesian models. Statistics and Computing 2014; 24: 997-1016. (2) Vehtari A, Gelman A. WAIC and cross-validation in Stan. Submitted. 2014. Accessed: 6 July 2015. My question in short is: The example […]

0.05 is a joke

Jim Delaney points to this tutorial by F. Perry Wilson on why the use of a “p less than 0.05” threshold does not imply a false positive rate of 5%, even if all the assumptions of the model are true. This is standard stuff but it’s always good to see it one more time. Delaney […]

Good advice can do you bad

Here are some examples of good, solid, reasonable statistical advice which can lead people astray. Example 1 Good advice: Statistical significance is not the same as practical significance. How it can mislead: People get the impression that a statistically significant result is more impressive if it’s larger in magnitude. Why it’s misleading: See this classic […]

Postdoctoral Researcher and Research Fellow positions in Computer Science in Helsinki, Finland

There are several PostDoc positions open in Aalto University and University of Helsinki related to statistical modeling, Bayesian inference, probabilistic programming (including Stan) and machine learning. There is also possibility to collaborate with me :) See a detailed list of the research areas and the full call text. The deadline is April 1, 2016. My […]

Bayesian inference for network links

A colleague writes: I’m working with a doctoral student on a latent affinity network problem and we keep hitting challenges in sampling, in our case using Metropolis-Hastings, for the network links. As you can imagine, lots of local modes, things get stuck, etc . . . Any suggestions on how to sample network links? My […]

Stan – The Bayesian Data Scientist’s Best Friend

My friend Juuso Parkkinen has interesting Stan related blog, which is worth following. The above cool animation is from today’s post discussing the updated results of using Stan to model apartment prices in Finland. Few weeks ago Juuso also blogged about a probabilistic programming seminar in Finland with a title Stan – The Bayesian Data […]

“The Bayesian Second Law of Thermodynamics”

Someone pointed me to this paper (by Anthony Bartolotta, Sean Carroll, Stefan Leichenauer, and Jason Pollack) and asked me what I thought. I didn’t have the time to look into it in any detail, but based on the title it seemed a bit Jaynesian. I sent it to a statistician and former physicist, who wrote: […]

No, this post is not 30 days early: Psychological Science backs away from null hypothesis significance testing

A few people pointed me to this editorial by D. Stephen Lindsay, the new editor of Psychological Science, a journal that in recent years has been notorious for publishing (and, even more notoriously, promoting) click-bait unreplicable dead-on-arrival noise-mining tea-leaf-reading research papers. It was getting so bad for awhile that they’d be publishing multiple such studies […]

He’s looking for a textbook that explains Bayesian methods for non-parametric tests

Brandon Vaughan writes: I am in the market for a textbook that explains Bayesian methods for non-parametric tests. My experience with Bayesian statistics thus far comes from John Krushke’s Doing Bayesian Data Analysis, but this book excludes non-parametric statistics. I do see that your text, Bayesian Data Analysis 3e, covers non-parametric statistics, however, does it […]

Fitting the birthday model in Stan

I’m scheduling these posts a few months ahead of time, and I realize this is the perfect date for an update on the birthday model. Can we fit in Stan yet? As of this writing, I don’t know. But Aki and Seth assure me that we’re close . . . P.S. Happy 13th birthday, Craig!

Probability paradox may be killing thousands

Brian Kinghorn points to this news article by Christian Grothoff and J. M. Porup, “The NSA’s SKYNET program may be killing thousands of innocent people; ‘Ridiculously optimistic’ machine learning algorithm is ‘completely bullshit,’ says expert.” The article begins: In 2014, the former director of both the CIA and NSA proclaimed that “we kill people based […]

Mister P: Challenges in Generalizing from Sample to Population (my talk at the Ross-Royall Symposium at Johns Hopkins this Friday)

Mister P: Challenges in Generalizing from Sample to Population Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University With internet surveys, nonrepresentativeness and nonresponse are bigger concerns than ever. The natural approach is to adjust for more information, demographic and otherwise, to align the sample with the population. We demonstrate the effectiveness […]

“Don’t get me started on ‘cut'”

Brendan Rocks writes: I have a request for a blog post. I’ve been following the debates about ‘cut’ on the Stan lists over the last few years. Lots of very clever people agree that it’s bad news, which is enough to put me off. However, I’ve never fully groked the reasoning. [I think that should […]

“What is Bayesian data analysis? Some examples”: My lecture at the New School this Wed noon

What is Bayesian data analysis? Some examples This is for their econ program, I think? I’ll demonstrate the three stages of Bayesian data analysis, going over examples such as the world cup analysis, the monster study, spell checking, the so-called global climate challenge, trends in death rates, . . . we’ll see how much time […]