Bayesian Statistics – StatsBlogs

The garden of 603,979,752 forking paths

Andrew — Thu, 27 Jun 2019 13:18:43 +0000

Amy Orben and Andrew Przybylski write: The widespread use of digital technologies by young people has spurred speculation that their regular use negatively impacts psychological well-being. Current empirical evidence supporting this idea is largely based on secondary analyses of large-scale social datasets. Though these datasets provide a valuable resource for highly powered investigations, their many […]

The post The garden of 603,979,752 forking paths appeared first on StatsBlogs.

How does Stan work? A reading list.

Andrew — Wed, 26 Jun 2019 13:14:46 +0000

Bob writes, to someone who is doing work on the Stan language: The basic execution structure of Stan is in the JSS paper (by Bob Carpenter, Andrew Matt Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell) and in the reference manual. The details of autodiff are in […]

The post How does Stan work? A reading list. appeared first on StatsBlogs.

AnnoNLP conference on data coding for natural language processing

Bob Carpenter — Tue, 25 Jun 2019 19:00:16 +0000

This workshop should be really interesting: Aggregating and analysing crowdsourced annotations for NLP EMNLP Workshop. November 3–4, 2019. Hong Kong. Silviu Paun and Dirk Hovy are co-organizing it. They’re very organized and know this area as well as anyone. I’m on the program committee, but won’t be able to attend. I really like the problem […]

The post AnnoNLP conference on data coding for natural language processing appeared first on StatsBlogs.

We should be open-minded, but not selectively open-minded.

Andrew — Mon, 24 Jun 2019 19:19:44 +0000

I wrote this post awhile ago but it just appeared . . . I liked this line so much I’m posting it on its own: We should be open-minded, but not selectively open-minded. This is related to the research incumbency effect and all sorts of other things we’ve talked about over the years. There’s a […]

The post We should be open-minded, but not selectively open-minded. appeared first on StatsBlogs.

Pharmacometrics meeting in Paris on the afternoon of 11 July 2019

Andrew — Sat, 15 Jun 2019 16:43:33 +0000

Julie Bertrand writes: The pharmacometrics group led by France Mentre (IAME, INSERM, Univ Paris) is very pleased to host a free ISoP Statistics and Pharmacometrics (SxP) SIG local event at Faculté Bichat, 16 rue Henri Huchard, 75018 Paris, on Thursday afternoon the 11th of July 2019. It will features talks from Professor Andrew Gelman, Univ […]

The post Pharmacometrics meeting in Paris on the afternoon of 11 July 2019 appeared first on StatsBlogs.

New! from Bales/Pourzanjani/Vehtari/Petzold: Selecting the Metric in Hamiltonian Monte Carlo

Andrew — Fri, 31 May 2019 23:29:25 +0000

Ben Bales, Arya Pourzanjani, Aki Vehtari, Linda Petzold write: We present a selection criterion for the Euclidean metric adapted during warmup in a Hamiltonian Monte Carlo sampler that makes it possible for a sampler to automatically pick the metric based on the model and the availability of warmup draws. Additionally, we present a new adaptation […]

The post New! from Bales/Pourzanjani/Vehtari/Petzold: Selecting the Metric in Hamiltonian Monte Carlo appeared first on StatsBlogs.

Peter Ellis on Forecasting Antipodal Elections with Stan

Bob Carpenter — Tue, 28 May 2019 18:48:36 +0000

I liked this intro to Peter Ellis from Rob J. Hyndman’s talk announcement: He [Peter Ellis] started forecasting elections in New Zealand as a way to learn how to use Stan, and the hobby has stuck with him since he moved back to Australia in late 2018. You may remember Peter from my previous post […]

The post Peter Ellis on Forecasting Antipodal Elections with Stan appeared first on StatsBlogs.

Neural nets vs. regression models

Andrew — Tue, 21 May 2019 13:55:46 +0000

Eliot Johnson writes: I have a question concerning papers comparing two broad domains of modeling: neural nets and statistical models. Both terms are catch-alls, within each of which there are, quite obviously, multiple subdomains. For instance, NNs could include ML, DL, AI, and so on. While statistical models should include panel data, time series, hierarchical […]

The post Neural nets vs. regression models appeared first on StatsBlogs.

“Did Jon Stewart elect Donald Trump?”

Andrew — Fri, 17 May 2019 13:27:26 +0000

I wrote this post a couple weeks ago and scheduled it for October, but then I learned from a reporter that the research article under discussion was retracted, so it seemed to make sense to post this right away while it was still newsworthy. My original post is below, followed by a post script regarding […]

The post “Did Jon Stewart elect Donald Trump?” appeared first on StatsBlogs.

Scandal! Mister P appears in British tabloid.

Andrew — Tue, 14 May 2019 13:43:55 +0000

Tim Morris points us to this news article: And here’s the kicker: Mister P. Not quite as cool as the time I was mentioned in Private Eye, but it’s still pretty satisfying. My next goal: Getting a mention in Sports Illustrated. (More on this soon.) In all seriousness, it’s so cool when methods that my […]

The post Scandal! Mister P appears in British tabloid. appeared first on StatsBlogs.

Stan examples in Harezlak, Ruppert and Wand (2018) Semiparametric Regression with R

Bob Carpenter — Mon, 06 May 2019 18:00:17 +0000

I saw earlier drafts of this when it was in preparation and they were great. Jarek Harezlak, David Ruppert and Matt P. Wand. 2018. Semiparametric Regression with R. UseR! Series. Springer. I particularly like the careful evaluation of variational approaches. I also very much like that it’s packed with visualizations and largely based on worked […]

The post Stan examples in Harezlak, Ruppert and Wand (2018) Semiparametric Regression with R appeared first on StatsBlogs.

A thought on Bayesian workflow: calculating a likelihood ratio for data compared to peak likelihood.

Andrew — Thu, 02 May 2019 18:40:29 +0000

Daniel Lakeland writes: Ok, so it’s really deep into the comments and I’m guessing there’s a chance you will miss it so I wanted to point at my comments here and here. In particular, the second one, which suggests something that it might be useful to recommend for Bayesian workflows: calculating a likelihood ratio for […]

The post A thought on Bayesian workflow: calculating a likelihood ratio for data compared to peak likelihood. appeared first on StatsBlogs.

Several post-doc positions in probabilistic programming etc. in Finland

Aki Vehtari — Thu, 25 Apr 2019 15:38:38 +0000

There are several open post-doc positions in Aalto and University of Helsinki in 1. probabilistic programming, 2. simulator-based inference, 3. data-efficient deep learning, 4. privacy preserving and secure methods, 5. interactive AI. All these research programs are connected and collaborating. I (Aki) am the coordinator for the project 1 and contributor in the others. Overall […]

The post Several post-doc positions in probabilistic programming etc. in Finland appeared first on StatsBlogs.

“Sometimes all we have left are pictures and fear”: Dan Simpson talk in Columbia stat dept, 4pm Monday

Andrew — Thu, 18 Apr 2019 00:16:29 +0000

4:10pm Monday, April 22 in Social Work Bldg room 903: Data is getting weirder. Statistical models and techniques are more complex than they have ever been. No one understand what code does. But at the same time, statistical tools are being used by a wider range of people than at any time in the past. […]

The post “Sometimes all we have left are pictures and fear”: Dan Simpson talk in Columbia stat dept, 4pm Monday appeared first on StatsBlogs.

The network of models and Bayesian workflow, related to generative grammar for statistical models

Andrew — Tue, 16 Apr 2019 13:28:55 +0000

Ben Holmes writes: I’m a machine learning guy working in fraud prevention, and a member of some biostatistics and clinical statistics research groups at Wright State University in Dayton, Ohio. I just heard your talk “Theoretical Statistics is the Theory of Applied Statistics” on YouTube, and was extremely interested in the idea of a model-space […]

The post The network of models and Bayesian workflow, related to generative grammar for statistical models appeared first on StatsBlogs.

State-space models in Stan

Andrew — Mon, 15 Apr 2019 13:22:29 +0000

Michael Ziedalski writes: For the past few months I have been delving into Bayesian statistics and have (without hyperbole) finally found statistics intuitive and exciting. Recently I have gone into Bayesian time series methods; however, I have found no libraries to use that can implement those models. Happily, I found Stan because it seemed among […]

The post State-space models in Stan appeared first on StatsBlogs.

All statistical conclusions require assumptions.

Andrew — Sun, 14 Apr 2019 13:04:35 +0000

Mark Palko points us to this 2009 article by Itzhak Gilboa, Andrew Postlewaite, and David Schmeidler, which begins: This note argues that, under some circumstances, it is more rational not to behave in accordance with a Bayesian prior than to do so. The starting point is that in the absence of information, choosing a prior […]

The post All statistical conclusions require assumptions. appeared first on StatsBlogs.

All statistical conclusions require assumptions.

Andrew — Sun, 14 Apr 2019 13:04:35 +0000

The post All statistical conclusions require assumptions. appeared first on StatsBlogs.

Active learning and decision making with varying treatment effects!

Andrew — Fri, 12 Apr 2019 02:11:27 +0000

In a new paper, Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, and Samuel Kaski write: Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based decision-making in a task of deciding which action a to take for a target […]

The post Active learning and decision making with varying treatment effects! appeared first on StatsBlogs.

Some Stan and Bayes short courses!

Andrew — Thu, 04 Apr 2019 23:05:07 +0000

Robert Grant writes: I have a couple of events coming up that people might be interested in. They are all at bayescamp.com/courses Stan Taster Webinar is on 15 May, runs for one hour and is only £15. I’ll demo Stan through R (and maybe PyStan and CmdStan if the interest is there on the day), […]

The post Some Stan and Bayes short courses! appeared first on StatsBlogs.