Skip to content
Archive of posts filed under the Bayesian Statistics category.

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 […]

Hierarchical models for phylogeny: Here’s what everyone’s talking about

The other day on the Stan users list, we had a long discussion on hierarchical models in phylogeny that I thought might be of general interest, so I’m reconstructing it here. It started with this question from Ben Lambert: I am hoping that you can help me settle a debate. My collaborators and I have […]

Pooling is relative to the model

Ryan Raaum writes: I’m hoping you’ll be willing to shed some light on a question I have regarding “pooling” in modeling. In your book with Jennifer Hill, you lay out two ends of a spectrum for dealing with structured data: (1) “Complete pooling” – ignoring the groups and pooling everything together for an overall average […]

Phd positions in Probabilistic Machine Learning at #AaltoPML group Finland

There are PhD positions in our Probabilistic Machine Learning group at Aalto, Finland, and altogether 15 positions in Helsinki ICT network. Apply here The most interesting topic in the call is supervised by Prof. Samuel Kaski at AaltoPML (and you may collaborate with me too :) We are looking for PhD candidates interested in probabilistic […]

Hey—go to Iceland and work on glaciers!

Egil Ferkingstad and Birgir Hrafnkelsson write: We have an exciting PhD position here at the University of Iceland on developing Bayesian hierarchical spatio-temporal models to the field of glaciology. Havard Rue at NTNU, Trondheim and Chris Wikle at the University of Missouri will also be part of the project. The Department of Mathematics at the […]

Summer internship positions for undergraduate students with Aki

There are couple cool summer internship positions for undergraduate students (BSc level) in Probabilistic Machine Learning group at Aalto (Finland) with me (Aki) and Samuel Kaski. Possible research topics are related to Bayesian inference, machine learning, Stan, disease risk prediction, personalised medicine, computational biology, contextual information retrieval, information visualization, etc. Application deadline 18 February. See more […]

Stunning breakthrough: Using Stan to map cancer screening!

Paul Alper points me to this article, Breast Cancer Screening, Incidence, and Mortality Across US Counties, by Charles Harding, Francesco Pompei, Dmitriy Burmistrov, Gilbert Welch, Rediet Abebe, and Richard Wilson. Their substantive conclusion is there’s too much screening going on, but here I want to focus on their statistical methods: Spline methods were used to […]

TOP SECRET: Newly declassified documents on evaluating models based on predictive accuracy

We recently had an email discussion among the Stan team regarding the use of predictive accuracy in evaluating computing algorithms. I thought this could be of general interest so I’m sharing it here. It started when Bob said he’d been at a meting on probabilistic programming where there was confusion on evaluation. In particular, some […]