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Archive of posts filed under the Bayesian Statistics category.

“A strong anvil need not fear the hammer”

Wagenmakers et al. write: A single experiment cannot overturn a large body of work. . . . An empirical debate is best organized around a series of preregistered replications, and perhaps the authors whose work we did not replicate will feel inspired to conduct their own preregistered studies. In our opinion, science is best served […]

“Bayesians (quite rightly so according to the theory) . . .”

Stephen Senn writes, “Bayesians (quite rightly so according to the theory) have every right to disagree with each other.” He could also add, “Non-Bayesians (quite rightly so according to the theory) have every right to disagree with each other.” Non-Bayesian statistics, like Bayesian statistics, uses models (or, if you prefer, methods). Different researchers will use […]

He’s looking for Bayesian time-series examples

Maurits Van Wagenberg writes: Coming from the traditional side, started to use Bayes, quickly limiting it to models with less variables, notwithstanding the lure. Am not in academics but have for many years researched design processes of complex objects such as engineering complex process plants. These processes have a lead-time from 12 to 18 months. […]

Data-dependent prior as an approximation to hierarchical model

Andy Solow writes: I have a question about Bayesian statistics. Why is it wrong to use the same data to formulate the prior and to update it to the posterior? I am having a hard time coming up with – or finding in the literature – a formal reason. I asked him to elaborate and […]

Thinking about this beautiful text sentiment visualizer yields a surprising insight about statistical graphics

Lucas Estevem set up this website in d3 as his final project in our statistical communication and graphics class this spring. Copy any text into the window, push the button, and you get this clean and attractive display showing the estimated positivity or negativity of each sentence. The length of each bar is some continuously-scaled […]

Noise noise noise noise noise

An intersting issue came up in comments to yesterday’s post. The story began with this query from David Shor: Suppose you’re conducting an experiment on the effectiveness of a pain medication, but in the post survey, measure a large number of indicators of well being (Sleep quality, self reported pain, ability to get tasks done, […]

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. http://www.stat.columbia.edu/~gelman/research/unpublished/waic_stan.pdf. 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 […]