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Archive of posts filed under the Bayesian Statistics category.

Boostrapping your posterior

Demetri Spanos writes: I bumped into your paper with John Carlin, Beyond Power Calculations, and encountered your concept of the hypothetical replication of the point estimate. In my own work I have used a similarly structured (but for technical reasons, differently motivated) concept which I have informally been calling the “consensus posterior.” Specifically, supposing a […]

Smooth poll aggregation using state-space modeling in Stan, from Jim Savage

Jim Savage writes: I just saw your post on poll bounces; have been thinking the same myself. Why are the poll aggregators so jumpy about new polls? Annoyed, I put together a poll aggregator that took a state-space approach to the unobserved preferences; nothing more than the 8 schools (14 polls?) example with a time-varying […]

The p-value is a random variable

Sam Behseta sends along this paper by Laura Lazzeroni, Ying Lu, and Ilana Belitskaya-Lévy, who write: P values from identical experiments can differ greatly in a way that is surprising to many. The failure to appreciate this wide variability can lead researchers to expect, without adequate justification, that statistically significant findings will be replicated, only […]

Guy Fieri wants your help! For a TV show on statistical models for real estate

I got the following email from David Mulholland: I’m a producer at Citizen Pictures where we produce Food Network’s “Diners, Dives and Drive-Ins” and Bravo’s digital series, “Going Off The Menu,” among others. A major network is working with us to develop a show that pits “data” against a traditional real estate agent to see […]

Amazon NYC decision analysis jobs

Dean Foster writes: Amazon is having a hiring event (Sept 8/9) here in NYC. If you are interested in working on demand forecasting either here in NYC or in Seattle send your resume to rcariapp@amazon.com by September 1st, 2016. Here’s the longer blurb: Amazon Supply Chain Optimization Technologies (SCOT) builds systems that automate decisions in […]

All maps of parameter estimates remain misleading

Roland Rau writes: After many years of applying frequentist statistical methods in mortality research, I just began to learn about the application of Bayesian methods in demography. Since I also wanted to change a part of my research focus on spatial models, I discovered your 1999 paper with Phil Price, All maps of parameter estimates […]

Fish cannot carry p-values

Following up on our discussion from last week on inference for fisheries, Anders Lamberg writes: Since I first sent you the question, there has been a debate here too. In the discussion you send, there is a debate both about the actual sampling (the mathematics) and about more the practical/biological issues. How accurate can farmed […]

Bayesian Inference with Stan for Pharmacometrics Class

Bob Carpenter, Daniel Lee, and Michael Betancourt will be teaching the 3-day class starting on 19 September in Paris. Following is the outline for the course: Day 1 Introduction to Bayesian statistics Likelihood / sampling distributions Priors, Posteriors via Bayes’s rule Posterior expectations and quantiles Events as expectations of indicator functions Introduction to Stan Basic […]

When do statistical rules affect drug approval?

Someone writes in: I have MS and take a disease-modifying drug called Copaxone. Sandoz developed a generic version​ of Copaxone​ and filed for FDA approval. Teva, the manufacturer of Copaxone, filed a petition opposing that approval (surprise!). FDA rejected Teva’s petitions and approved the generic. My insurance company encouraged me to switch to the generic. […]

Going beyond confidence intervals

Anders Lamberg writes: In an article by Tom Sigfried, Science News, July 3 2014, “Scientists’ grasp of confidence intervals doesn’t inspire confidence” you are cited: “Gelman himself makes the point most clearly, though, that a 95 percent probability that a confidence interval contains the mean refers to repeated sampling, not any one individual interval.” I […]

Bayesian Linear Mixed Models using Stan: A tutorial for psychologists, linguists, and cognitive scientists

This article by Tanner Sorensen, Sven Hohenstein, and Shravan Vasishth might be of interest to some of you.

Moving statistical theory from a “discovery” framework to a “measurement” framework

Avi Adler points to this post by Felix Schönbrodt on “What’s the probability that a significant p-value indicates a true effect?” I’m sympathetic to the goal of better understanding what’s in a p-value (see for example my paper with John Carlin on type M and type S errors) but I really don’t like the framing […]

“Pointwise mutual information as test statistics”

Christian Bartels writes: Most of us will probably agree that making good decisions under uncertainty based on limited data is highly important but remains challenging. We have decision theory that provides a framework to reduce risks of decisions under uncertainty with typical frequentist test statistics being examples for controlling errors in absence of prior knowledge. […]

One-day workshop on causal inference (NYC, Sat. 16 July)

James Savage is teaching a one-day workshop on causal inference this coming Saturday (16 July) in New York using RStanArm. Here’s a link to the details: One-day workshop on causal inference Here’s the course outline: How do prices affect sales? What is the uplift from a marketing decision? By how much will studying for an […]

Causal and predictive inference in policy research

Todd Rogers pointed me to a paper by Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, and Ziad Obermeyer that begins: Empirical policy research often focuses on causal inference. Since policy choices seem to depend on understanding the counterfactual—what happens with and without a policy—this tight link of causality and policy seems natural. While this link holds […]

Reproducible Research with Stan, R, knitr, Docker, and Git (with free GitLab hosting)

Jon Zelner recently developed a neat Docker packaging of Stan, R, and knitr for fully reproducible research. The first in his series of posts (with links to the next parts) is here: * Reproducibility, part 1 The post on making changes online and auto-updating results using GitLab’s continuous integration service is here: * GitLab continuous […]

Causal mediation

Judea Pearl points me to this discussion with Kosuke Imai at a conference on causal mediation. I continue to think that the most useful way to think about mediation is in terms of a joint or multivariate outcome, and I continue to think that if we want to understand mediation, we need to think about […]

Too good to be true: when overwhelming mathematics fails to convince

Gordon Danning points me to this news article by Lisa Zyga, “Why too much evidence can be a bad thing,” reporting on a paper by Lachlan Gunn and others. Their conclusions mostly seem reasonable, if a bit exaggerated. For example, I can’t believe this: The researchers demonstrated the paradox in the case of a modern-day […]

“Simple, Scalable and Accurate Posterior Interval Estimation”

Cheng Li, Sanvesh Srivastava, and David Dunson write: We propose a new scalable algorithm for posterior interval estimation. Our algorithm first runs Markov chain Monte Carlo or any alternative posterior sampling algorithm in parallel for each subset posterior, with the subset posteriors proportional to the prior multiplied by the subset likelihood raised to the full […]

Informative priors for treatment effects

Biostatistician Garnett McMillan writes: A PI recently completed a randomized trial where the experimental treatment showed a large, but not quite statistically significant (p=0.08) improvement over placebo. The investigators wanted to know how many additional subjects would be needed to achieve significance. This is a common question, which is very hard to answer for non-statistical […]