Category: Statistics

Back by popular demand . . . The Greatest Seminar Speaker contest!

Regular blog readers will remember our seminar speaker competition from a few years ago. Here was our bracket, back in 2015: And here were the 64 contestants: – Philosophers: Plato (seeded 1 in group) Alan Turing (seeded 2) Aristotle (3) Friedrich Nietzsche (4) Thomas Hobbes Jean-Jacques Rousseau Bertrand Russell Karl Popper – Religious Leaders: Mohandas […]

The post Back by popular demand . . . The Greatest Seminar Speaker contest! appeared first on Statistical Modeling, Causal Inference, and Social Science.

SIST* Blog Posts: Excerpts & Mementos (to Dec 31 2018)

Excerpts 05/19: The Meaning of My Title: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars 09/08: Excursion 1 Tour I: Beyond Probabilism and Performance: Severity Requirement (1.1) 09/11: Excursion 1 Tour I (2nd stop): Probabilism, Performance, and Probativeness (1.2) 09/15: Excursion 1 Tour I (3rd stop): The Current State of Play in Statistical Foundations: […]

Robin Pemantle’s updated bag of tricks for math teaching!

Here it is! He’s got the following two documents: – Tips for Active Learning in the College Setting – Tips for Active Learning in Teacher Prep or in the K-12 Setting This is great stuff (see my earlier review here). Every mathematician and math teacher in the universe should read this. So, if any of […]

The post Robin Pemantle’s updated bag of tricks for math teaching! appeared first on Statistical Modeling, Causal Inference, and Social Science.

Robin Pemantle’s updated bag of tricks for math teaching!

Here it is! He’s got the following two documents: – Tips for Active Learning in the College Setting – Tips for Active Learning in Teacher Prep or in the K-12 Setting This is great stuff (see my earlier review here). Every mathematician and math teacher in the universe should read this. So, if any of […]

The post Robin Pemantle’s updated bag of tricks for math teaching! appeared first on Statistical Modeling, Causal Inference, and Social Science.

mixture modelling for testing hypotheses

After a fairly long delay (since the first version was posted and submitted in December 2014), we eventually revised and resubmitted our paper with Kaniav Kamary [who has now graduated], Kerrie Mengersen, and Judith Rousseau on the final day of 2018. The main reason for this massive delay is mine’s, as I got fairly depressed […]

Monte Carlo fusion

Hongsheng Dai, Murray Pollock (University of Warwick), and Gareth Roberts (University of Warwick) just arXived a paper we discussed together last year while I was at Warwick. Where fusion means bringing different parts of the target distribution f(x)∝f¹(x)f²(x)… together, once simulation from each part has been done. In the same spirit as in Scott et […]

Published in 2018

R-squared for Bayesian regression models. {\em American Statistician}. (Andrew Gelman, Ben Goodrich, Jonah Gabry, and Aki Vehtari) Voter registration databases and MRP: Toward the use of large scale databases in public opinion research. {\em Political Analysis}. (Yair Ghitza and Andrew Gelman) Limitations of “Limitations of Bayesian leave-one-out cross-validation for model selection.” {\em Computational Brain and […]

The post Published in 2018 appeared first on Statistical Modeling, Causal Inference, and Social Science.

Published in 2018

R-squared for Bayesian regression models. {\em American Statistician}. (Andrew Gelman, Ben Goodrich, Jonah Gabry, and Aki Vehtari) Voter registration databases and MRP: Toward the use of large scale databases in public opinion research. {\em Political Analysis}. (Yair Ghitza and Andrew Gelman) Limitations of “Limitations of Bayesian leave-one-out cross-validation for model selection.” {\em Computational Brain and […]

The post Published in 2018 appeared first on Statistical Modeling, Causal Inference, and Social Science.

What to do when you read a paper and it’s full of errors and the author won’t share the data or be open about the analysis?

Someone writes: I would like to ask you for an advice regarding obtaining data for reanalysis purposes from an author who has multiple papers with statistical errors and doesn’t want to share the data. Recently, I reviewed a paper that included numbers that had some of the reported statistics that were mathematically impossible. As the […]

The post What to do when you read a paper and it’s full of errors and the author won’t share the data or be open about the analysis? appeared first on Statistical Modeling, Causal Inference, and Social Science.

What to do when you read a paper and it’s full of errors and the author won’t share the data or be open about the analysis?

Someone writes: I would like to ask you for an advice regarding obtaining data for reanalysis purposes from an author who has multiple papers with statistical errors and doesn’t want to share the data. Recently, I reviewed a paper that included numbers that had some of the reported statistics that were mathematically impossible. As the […]

The post What to do when you read a paper and it’s full of errors and the author won’t share the data or be open about the analysis? appeared first on Statistical Modeling, Causal Inference, and Social Science.

Exponential sums in 2019

I’ve made a small change in my exponential sum page. I’ll need to give a little background before explaining the change. First of all, you can read exactly what these exponential sums are here. These plots can be periodic in two senses. The first is simply repeating the same sequence of points. The second is […]

O’Bayes 2019: speakers, discussants, posters!

The program for the next O’Bayes conference in Warwick, 28 June-02 July, 2019, is now set. Speakers and discussants have been contacted by the scientific committee and accepted our invitation! As usual, there will be poster sessions on the nights of 29 and 30 June and the call is open for poster submissions, until January […]

“Principles of posterior visualization”

What better way to start the new year than with a discussion of statistical graphics. Mikhail Shubin has this great post from a few years ago on Bayesian visualization. He lists the following principles: Principle 1: Uncertainty should be visualized Principle 2: Visualization of variability ≠ Visualization of uncertainty Principle 3: Equal probability = Equal […]

The post “Principles of posterior visualization” appeared first on Statistical Modeling, Causal Inference, and Social Science.

“Principles of posterior visualization”

What better way to start the new year than with a discussion of statistical graphics. Mikhail Shubin has this great post from a few years ago on Bayesian visualization. He lists the following principles: Principle 1: Uncertainty should be visualized Principle 2: Visualization of variability ≠ Visualization of uncertainty Principle 3: Equal probability = Equal […]

The post “Principles of posterior visualization” appeared first on Statistical Modeling, Causal Inference, and Social Science.