Category: Bayesian Statistics

Continuous tempering through path sampling

Yuling prepared this poster summarizing our recent work on path sampling using a continuous joint distribution. The method is really cool and represents a real advance over what Xiao-Li and I were doing in our 1998 paper. It’s still gonna have problems in high or even moderate dimensions, and ultimately I think we’re gonna need […]

The post Continuous tempering through path sampling appeared first on Statistical Modeling, Causal Inference, and Social Science.

Continuous tempering through path sampling

Yuling prepared this poster summarizing our recent work on path sampling using a continuous joint distribution. The method is really cool and represents a real advance over what Xiao-Li and I were doing in our 1998 paper. It’s still gonna have problems in high or even moderate dimensions, and ultimately I think we’re gonna need […]

The post Continuous tempering through path sampling appeared first on Statistical Modeling, Causal Inference, and Social Science.

Awesome MCMC animation site by Chi Feng! On Github!

Sean Talts and Bob Carpenter pointed us to this awesome MCMC animation site by Chi Feng. For instance, here’s NUTS on a banana-shaped density. This is indeed super-cool, and maybe there’s a way to connect these with Stan/ShinyStan/Bayesplot so as to automatically make movies of Stan model fits. This would be great, both to help […]

The post Awesome MCMC animation site by Chi Feng! On Github! appeared first on Statistical Modeling, Causal Inference, and Social Science.

Awesome MCMC animation site by Chi Feng! On Github!

Sean Talts and Bob Carpenter pointed us to this awesome MCMC animation site by Chi Feng. For instance, here’s NUTS on a banana-shaped density. This is indeed super-cool, and maybe there’s a way to connect these with Stan/ShinyStan/Bayesplot so as to automatically make movies of Stan model fits. This would be great, both to help […]

The post Awesome MCMC animation site by Chi Feng! On Github! appeared first on Statistical Modeling, Causal Inference, and Social Science.

Parsimonious principle vs integration over all uncertainties

tl;dr If you have bad models, bad priors or bad inference choose the simplest possible model. If you have good models, good priors, good inference, use the most elaborate model for predictions. To make interpretation easier you may use a smaller model with similar predictive performance as the most elaborate model. Merijn Mestdagh emailed me […]

The post Parsimonious principle vs integration over all uncertainties appeared first on Statistical Modeling, Causal Inference, and Social Science.

Parsimonious principle vs integration over all uncertainties

tl;dr If you have bad models, bad priors or bad inference choose the simplest possible model. If you have good models, good priors, good inference, use the most elaborate model for predictions. To make interpretation easier you may use a smaller model with similar predictive performance as the most elaborate model. Merijn Mestdagh emailed me […]

The post Parsimonious principle vs integration over all uncertainties appeared first on Statistical Modeling, Causal Inference, and Social Science.

“The idea of replication is central not just to scientific practice but also to formal statistics . . . Frequentist statistics relies on the reference set of repeated experiments, and Bayesian statistics relies on the prior distribution which represents the population of effects.”

Rolf Zwaan (who we last encountered here in “From zero to Ted talk in 18 simple steps”), Alexander Etz, Richard Lucas, and M. Brent Donnellan wrote an article, “Making replication mainstream,” which begins: Many philosophers of science and methodologists have argued that the ability to repeat studies and obtain similar results is an essential component […]

The post “The idea of replication is central not just to scientific practice but also to formal statistics . . . Frequentist statistics relies on the reference set of repeated experiments, and Bayesian statistics relies on the prior distribution which represents the population of effects.” appeared first on Statistical Modeling, Causal Inference, and Social Science.

Mister P wins again

Chad Kiewiet De Jonge, Gary Langer, and Sofi Sinozich write: This paper presents state-level estimates of the 2016 presidential election using data from the ABC News/Washington Post tracking poll and multilevel regression with poststratification (MRP). While previous implementations of MRP for election forecasting have relied on data from prior elections to establish poststratification targets for […]

The post Mister P wins again appeared first on Statistical Modeling, Causal Inference, and Social Science.

Joint inference or modular inference? Pierre Jacob, Lawrence Murray, Chris Holmes, Christian Robert discuss conditions on the strength and weaknesses of these choices

Pierre Jacob, Lawrence Murray, Chris Holmes, Christian Robert write: In modern applications, statisticians are faced with integrating heterogeneous data modalities relevant for an inference, prediction, or decision problem. In such circumstances, it is convenient to use a graphical model to represent the statistical dependencies, via a set of connected “modules”, each relating to a specific […]

The post Joint inference or modular inference? Pierre Jacob, Lawrence Murray, Chris Holmes, Christian Robert discuss conditions on the strength and weaknesses of these choices appeared first on Statistical Modeling, Causal Inference, and Social Science.

Divisibility in statistics: Where is it needed?

The basics of Bayesian inference is p(parameters|data) proportional to p(parameters)*p(data|parameters). And, for predictions, p(predictions|data) = integral_parameters p(predictions|parameters,data)*p(parameters|data). In these expressions (and the corresponding simpler versions for maximum likelihood), “parameters” and “data” are unitary objects. Yes, it can be helpful to think of the parameter objects as being a list or vector of individual parameters; and […]

The post Divisibility in statistics: Where is it needed? appeared first on Statistical Modeling, Causal Inference, and Social Science.

All of Life is 6 to 5 Against

Donny Williams writes: I have a question I have been considering asking you for a while. The more I have learned about Bayesian methods, including regularly reading the journal Bayesian Analysis (preparing a submission here, actually!), etc., I have come to not only see that frequency properties are studied of Bayesian models, but it is […]

The post All of Life is 6 to 5 Against appeared first on Statistical Modeling, Causal Inference, and Social Science.

Anyone want to run this Bayesian computing conference in 2022?

OK, people think I’m obsessive with a blog with a 6-month lag, but that’s nothing compared to some statistics conferences. Mylène Bédard sends this along for anyone who might be interested: The Bayesian Computation Section of ISBA is soliciting proposals to host its flagship conference: Bayes Comp 2022 The expectation is that the meeting will […]

The post Anyone want to run this Bayesian computing conference in 2022? appeared first on Statistical Modeling, Causal Inference, and Social Science.

Yes, but did it work? Evaluating variational inference

That’s the title of a recent article by Yuling Yao, Aki Vehtari, Daniel Simpson, and myself, which presents some diagnostics for variational approximations to posterior inference: We were motivated to write this paper by the success/failure of ADVI, the automatic variational inference algorithm devised by Alp Kucukelbir et al. The success was that ADVI solved […]

The post Yes, but did it work? Evaluating variational inference appeared first on Statistical Modeling, Causal Inference, and Social Science.

Power analysis and NIH-style statistical practice: What’s the implicit model?

So. Following up on our discussion of “the 80% power lie,” I was thinking about the implicit model underlying NIH’s 80% power rule. Several commenters pointed out that, to have your study design approved by NSF, it’s not required that you demonstrate that you have 80% power for real; what’s needed is to show 80% […]

The post Power analysis and NIH-style statistical practice: What’s the implicit model? appeared first on Statistical Modeling, Causal Inference, and Social Science.

Bayesians are frequentists

Bayesians are frequentists. What I mean is, the Bayesian prior distribution corresponds to the frequentist sample space: it’s the set of problems for which a particular statistical model or procedure will be applied. I was thinking about this in the context of this question from Vlad Malik: I noticed this comment on Twitter in reference […]

The post Bayesians are frequentists appeared first on Statistical Modeling, Causal Inference, and Social Science.

Stan goes to the World Cup

Leo Egidi shares his 2018 World Cup model, which he’s fitting in Stan. But I don’t like this: First, something’s missing. Where’s the U.S.?? More seriously, what’s with that “16.74%” thing? So bogus. You might as well say you’re 66.31 inches tall. Anyway, as is often the case with Bayesian models, the point here is […]

The post Stan goes to the World Cup appeared first on Statistical Modeling, Causal Inference, and Social Science.

Stan Workshop on Pharmacometrics—Paris, 24 July 2018

What: A one-day event organized by France Mentre (IAME, INSERM, Univ SPC, Univ Paris 7, Univ Paris 13) and Julie Bertrand (INSERM) and sponsored by the International Society of Pharmacometrics (ISoP). When: Tuesday 24 July 2018 Where: Faculté Bichat, 16 rue Henri Huchard, 75018 Paris Free Registration: Registration is being handled by ISoP; please click […]

The post Stan Workshop on Pharmacometrics—Paris, 24 July 2018 appeared first on Statistical Modeling, Causal Inference, and Social Science.

Global shifts in the phenological synchrony of species interactions over recent decades

Heather Kharouba et al. write: Phenological responses to climate change (e.g., earlier leaf-out or egg hatch date) are now well documented and clearly linked to rising temperatures in recent decades. Such shifts in the phenologies of interacting species may lead to shifts in their synchrony, with cascading community and ecosystem consequences . . . We […]

The post Global shifts in the phenological synchrony of species interactions over recent decades appeared first on Statistical Modeling, Causal Inference, and Social Science.