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Archive of posts filed under the Bayesian Statistics category.

Everything I need to know about Bayesian statistics, I learned in eight schools.

This post is by Phil. I’m aware that there are some people who use a Bayesian approach largely because it allows them to provide a highly informative prior distribution based subjective judgment, but that is not the appeal of Bayesian methods for a lot of us practitioners. It’s disappointing and surprising, twenty years after my initial experiences, […]

Transformations for non-normal data

Steve Peterson writes: I recently submitted a proposal on applying a Bayesian analysis to gender comparisons on motivational constructs. I had an idea on how to improve the model I used and was hoping you could give me some feedback. The data come from a survey based on 5-point Likert scales. Different constructs are measured […]

Against overly restrictive definitions: No, I don’t think it helps to describe Bayes as “the analysis of subjective
 beliefs” (nor, for that matter, does it help to characterize the statements of Krugman or Mankiw as not being “economics”)

I get frustrated when people use aggressive overly restrictive definitions of something they don’t like. [I originally used the term "aggressive definitions" but I think the whole "aggressive" thing was misleading as it implies aggressive intent, which I did not mean to imply. So I changed to "overly restrictive definition."] Here’s an example of an […]

Postdoc involving pathbreaking work in MRP, Stan, and the 2014 election!

We’re working with polling company YouGov to track public opinion, state-by-state and district-by-district, during the 2014 campaign. We’ll be using multilevel regression and poststratification, and implementing it in Stan, and developing the necessary new parts of Stan to get this running scalably and efficiently. And we’ll be making the most detailed, up-to-date election forecasts. What […]

Judea Pearl overview on causal inference, and more general thoughts on the reexpression of existing methods by considering their implicit assumptions

This material should be familiar to many of you but could be helpful to newcomers. Pearl writes: ALL causal conclusions in nonexperimental settings must be based on untested, judgmental assumptions that investigators are prepared to defend on scientific grounds. . . . To understand what the world should be like for a given procedure to […]

Belief aggregation

Johannes Castner writes: Suppose there are k scientists, each with her own model (Bayesian Net) over m random variables. Then, because the space of Bayesian Nets over these m variables, with the square-root of the Jensen-Shannon Divergence as a distance metric is a closed and bounded space, there exists one unique Bayes Net that is […]

2013

There’s lots of overlap but I put each paper into only one category.  Also, I’ve included work that has been published in 2013 as well as work that has been completed this year and might appear in 2014 or later.  So you can can think of this list as representing roughly two years’ work. Political […]

No on Yes/No decisions

Just to elaborate on our post from last month (“I’m negative on the expression ‘false positives’”), here’s a recent exchange exchange we had regarding the relevance of yes/no decisions in summarizing statistical inferences about scientific questions. Shravan wrote: Isn’t it true that I am already done if P(theta>0) is much larger than P(theta

Statistical evidence for revised standards

In response to the discussion of X and me of his recent paper, Val Johnson writes: I would like to thank Andrew for forwarding his comments on uniformly most powerful Bayesian tests (UMPBTs) to me and his invitation to respond to them. I think he (and also Christian Robert) raise a number of interesting points […]

Estimating and summarizing inference for hierarchical variance parameters when the number of groups is small

Chris Che-Castaldo writes: I am trying to compute variance components for a hierarchical model where the group level has two binary predictors and their interaction. When I model each of these three predictors as N(0, tau) the model will not converge, perhaps because the number of coefficients in each batch is so small (2 for […]