Posts Tagged ‘ Bayesian statistics ’

Mortality rate trends by age, ethnicity, sex, and state (link fixed)

March 23, 2017
By
Mortality rate trends by age, ethnicity, sex, and state (link fixed)

There continues to be a lot of discussion on the purported increase in mortality rates among middle-aged white people in America. Actually an increase among women and not much change among men but you don’t hear so much about this as it contradicts the “struggling white men” story that we hear so much about in […] The post Mortality rate trends by age, ethnicity, sex, and state (link fixed) appeared…

Read more »

Some natural solutions to the p-value communication problem—and why they won’t work

March 21, 2017
By

Blake McShane and David Gal recently wrote two articles (“Blinding us to the obvious? The effect of statistical training on the evaluation of evidence” and “Statistical significance and the dichotomization of evidence”) on the misunderstandings of p-values that are common even among supposed experts in statistics and applied social research. The key misconception has nothing […] The post Some natural solutions to the p-value communication problem—and why they won’t work…

Read more »

“Bias” and “variance” are two ways of looking at the same thing. (“Bias” is conditional, “variance” is unconditional.)

March 18, 2017
By

Someone asked me about the distinction between bias and noise and I sent him some links. Then I thought this might interest some of you too, so here it is: Here’s a recent paper on election polling where we try to be explicit about what is bias and what is variance: And here are some […] The post “Bias” and “variance” are two ways of looking at the same thing.…

Read more »

“A blog post that can help an industry”

March 18, 2017
By

Tim Bock writes: I understood how to address weights in statistical tests by reading Lu and Gelman (2003). Thanks. You may be disappointed to know that this knowledge allowed me to write software, which has been used to compute many billions of p-values. When I read your posts and papers on forking paths, I always […] The post “A blog post that can help an industry” appeared first on Statistical…

Read more »

Ensemble Methods are Doomed to Fail in High Dimensions

March 15, 2017
By
Ensemble Methods are Doomed to Fail in High Dimensions

Ensemble methods By ensemble methods, I (Bob, not Andrew) mean approaches that scatter points in parameter space and then make moves by inteprolating or extrapolating among subsets of them. Two prominent examples are: Ter Braak’s differential evolution   Goodman and Weare’s walkers There are extensions and computer implementations of these algorithms. For example, the Python […] The post Ensemble Methods are Doomed to Fail in High Dimensions appeared first on…

Read more »

Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data

March 11, 2017
By
Expectation propagation as a way of life:  A framework for Bayesian inference on partitioned data

After three years, we finally have an updated version of our “EP as a way of life” paper. Authors are Andrew Gelman, Aki Vehtari, Pasi Jylänki, Tuomas Sivula, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John Cunningham, David Schiminovich, and Christian Robert. Aki deserves credit for putting this all together into a coherent whole. Here’s the […] The post Expectation propagation as a way of life: A framework for Bayesian inference…

Read more »

A fistful of Stan case studies: divergences and bias, identifying mixtures, and weakly informative priors

March 7, 2017
By

Following on from his talk at StanCon, Michael Betancourt just wrote three Stan case studies, all of which are must reads: Diagnosing Biased Inference with Divergences: This case study discusses the subtleties of accurate Markov chain Monte Carlo estimation and how divergences can be used to identify biased estimation in practice.   Identifying Bayesian Mixture […] The post A fistful of Stan case studies: divergences and bias, identifying mixtures, and…

Read more »

How to interpret confidence intervals?

March 4, 2017
By

Jason Yamada-Hanff writes: I’m a Neuroscience PhD reforming my statistics education. I am a little confused about how you treat confidence intervals in the book and was hoping you could clear things up for me. Through your blog, I found Richard Morey’s paper (and further readings) about confidence interval interpretations. If I understand correctly, the […] The post How to interpret confidence intervals? appeared first on Statistical Modeling, Causal Inference,…

Read more »

Yes, it makes sense to do design analysis (“power calculations”) after the data have been collected

March 3, 2017
By

This one has come up before but it’s worth a reminder. Stephen Senn is a thoughtful statistician and I generally agree with his advice but I think he was kinda wrong on this one. Wrong in an interesting way. Senn’s article is from 2002 and it is called “Power is indeed irrelevant in interpreting completed […] The post Yes, it makes sense to do design analysis (“power calculations”) after the…

Read more »

Facebook’s Prophet uses Stan

March 1, 2017
By
Facebook’s Prophet uses Stan

Sean Taylor, a research scientist at Facebook and Stan user, writes: I wanted to tell you about an open source forecasting package we just released called Prophet:  I thought the readers of your blog might be interested in both the package and the fact that we built it on top of Stan. Under the hood, […] The post Facebook’s Prophet uses Stan appeared first on Statistical Modeling, Causal Inference, and…

Read more »


Subscribe

Email:

  Subscribe