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Archive of posts filed under the Bayesian Statistics category.

Is Rigor Contagious? (my talk next Monday 4:15pm at Columbia)

Is Rigor Contagious? Much of the theory and practice of statistics and econometrics is characterized by a toxic mixture of rigor and sloppiness. Methods are justified based on seemingly pure principles that can’t survive reality. Examples of these principles include random sampling, unbiased estimation, hypothesis testing, Bayesian inference, and causal identification. Examples of uncomfortable reality […]

Looking for rigor in all the wrong places (my talk this Thursday in the Columbia economics department)

Looking for Rigor in All the Wrong Places What do the following ideas and practices have in common: unbiased estimation, statistical significance, insistence on random sampling, and avoidance of prior information? All have been embraced as ways of enforcing rigor but all have backfired and led to sloppy analyses and erroneous inferences. We discuss these […]

Blind Spot

X pointed me to this news article reporting an increase in death rate among young adults in the United States: Selon une enquête publiée le 26 janvier par la revue scientifique The Lancet, le taux de mortalité des jeunes Américains âgés de 25 à 35 ans a connu une progression entre 1999 et 2014, alors […]

Vine regression?

Jeremy Neufeld writes: I’m an undergraduate student at the University of Maryland and I was recently referred to this paper (Vine Regression, by Roger Cooke, Harry Joe, and Bo Chang), also an accompanying summary blog post by the main author) as potentially useful in policy analysis. With the big claims it makes, I am not […]

Krzysztof Sakrejda speaks in NYC on Bayesian hierarchical survival-type model for Dengue infection

Daniel writes: Krzysztof Sakrejda is giving a cool talk next Tues 5:30-7pm downtown on a survival model for Dengue infection using Stan. If you’re interested, please register asap. Google is asking for the names for security by tomorrow morning.

Combining results from multiply imputed datasets

Aaron Haslam writes: I have a question regarding combining the estimates from multiply imputed datasets. In the third addition of BDA on the top of page 452, you mention that with Bayesian analyses all you have to do is mix together the simulations. I want to clarify that this means you simply combine the posteriors […]

Lasso regression etc in Stan

Someone on the users list asked about lasso regression in Stan, and Ben replied: In the rstanarm package we have stan_lm(), which is sort of like ridge regression, and stan_glm() with family = gaussian and prior = laplace() or prior = lasso(). The latter estimates the shrinkage as a hyperparameter while the former fixes it […]

Stan and BDA on actuarial syllabus!

Avi Adler writes: I am pleased to let you know that the Casualty Actuarial Society has announced two new exams and released their initial syllabi yesterday. Specifically, 50%–70% of the Modern Actuarial Statistics II exam covers Bayesian Analysis and Markov Chain Monte Carlo. The official text we will be using is BDA3 and while we […]

Storytelling as predictive model checking

I finally got around to reading Adam Begley’s biography of John Updike, and it was excellent. I’ll have more on that in a future post, but for now I just went to share the point, which I’d not known before, that almost all of Updike’s characters and even the descriptions and events in many of […]

HMMs in Stan? Absolutely!

I was having a conversation with Andrew that went like this yesterday: Andrew: Hey, someone’s giving a talk today on HMMs (that someone was Yang Chen, who was giving a talk based on her JASA paper Analyzing single-molecule protein transportation experiments via hierarchical hidden Markov models). Maybe we should add some specialized discrete modules to […]

You can fit hidden Markov models in Stan (and thus, also in Stata! and Python! and R! and Julia! and Matlab!)

You can fit finite mixture models in Stan; see section 12 of the Stan manual. You can fit change point models in Stan; see section 14.2 of the Stan manual. You can fit mark-recapture models in Stan; see section 14.2 of the Stan manual. You can fit hidden Markov models in Stan; see section 9.6 […]

Theoretical statistics is the theory of applied statistics: how to think about what we do (My talk at the University of Michigan this Friday 3pm)

Theoretical statistics is the theory of applied statistics: how to think about what we do Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University Working scientists and engineers commonly feel that philosophy is a waste of time. But theoretical and philosophical principles can guide practice, so it makes sense for us to […]

Long Shot

Frank Harrell doesn’t like p-values: In my [Frank’s] opinion, null hypothesis testing and p-values have done significant harm to science. The purpose of this note is to catalog the many problems caused by p-values. As readers post new problems in their comments, more will be incorporated into the list, so this is a work in […]

No guru, no method, no teacher, Just you and I and nature . . . in the garden. Of forking paths.

Here’s a quote: Instead of focusing on theory, the focus is on asking and answering practical research questions. It sounds eminently reasonable, yet in context I think it’s completely wrong. I will explain. But first some background. Junk science and statistics They say that hard cases make bad law. But bad research can make good […]

Thanks for attending StanCon 2017!

Thank you all for coming and making the first Stan Conference a success! The organizers were blown away by how many people came to the first conference. We had over 150 registrants this year! StanCon 2017 Video The organizers managed to get a video stream on YouTube: https://youtu.be/DJ0c7Bm5Djk. We have over 1900 views since StanCon! (We lost […]

Quantifying uncertainty in identification assumptions—this is important!

Luis Guirola writes: I’m a poli sci student currently working on methods. I’ve seen you sometimes address questions in your blog, so here is one in case you wanted. I recently read some of Chuck Manski book “Identification for decision and prediction”. I take his main message to be “The only way to get identification […]

Is the dorsal anterior cingulate cortex “selective for pain”?

Peter Clayson writes: I have spent much of the last 6 months or so of my life trying to learn Bayesian statistics on my own. It’s been a difficult, yet rewarding experience. I have a question about a research debate that is going on my field. Briefly, the debate between some very prominent scholars in […]

Looking for rigor in all the wrong places

My talk in the upcoming conference on Inference from Non Probability Samples, 16-17 Mar in Paris: Looking for rigor in all the wrong places What do the following ideas and practices have in common: unbiased estimation, statistical significance, insistence on random sampling, and avoidance of prior information? All have been embraced as ways of enforcing […]

Come and work with us!

Stan is an open-source, state-of-the-art probabilistic programming language with a high-performance Bayesian inference engine written in C++. Stan had been successfully applied to modeling problems with hundreds of thousands of parameters in fields as diverse as econometrics, sports analytics, physics, pharmacometrics, recommender systems, political science, and many more. Research using Stan has been featured in […]

Laurie Davies: time series decomposition of birthday data

On the cover of BDA3 is a Bayesian decomposition of the time series of birthdays in the U.S. over a 20-year period. We modeled the data as a sum of Gaussian processes and fit it using GPstuff. Occasionally we fit this model to new data; see for example this discussion of Friday the 13th and […]