Statistical Modeling, Causal Inference, and Social Science
Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University.
Statistical Modeling, Causal Inference, and Social Science's Latest Posts
This post is by Phil Price. Perhaps prompted by the ALS Ice Bucket Challenge, this infographic has been making the rounds: I think this is one of the worst I have ever seen. I don’t know where it came from, so I can’t give credit/blame where it’s due. Let’s put aside the numbers themselves – […] The post One of the worst infographics ever, but people don’t care? appeared first on Statistical Modeling, Causal Inference, and Social Science.
Discussion of “A probabilistic model for the spatial distribution of party support in multiparty elections”
From 1994. I don’t have much to say about this one. The paper I was discussing (by Samuel Merrill) had already been accepted by the journal—I might even have been a referee, in which case the associate editor had decided to accept the paper over my objections—and the editor gave me the opportunity to publish […] The post Discussion of “A probabilistic model for the spatial distribution of party support in multiparty elections” appeared first on Statistical […]
Dave Blei writes: This course is cross listed in Computer Science and Statistics at Columbia University. It is a PhD level course about applied probabilistic modeling. Loosely, it will be similar to this course. Students should have some background in probability, college-level mathematics (calculus, linear algebra), and be comfortable with computer programming. The course is […] The post Dave Blei course on Foundations of Graphical Models appeared first on Statistical Modeling, Causal […]
From 1993. The topic of election forecasting sure gets a lot more attention than it used to! Here are some quotes from my review of that book by Michael Lewis-Beck and Tom Rice: Political scientists are aware that most voters are consistent in their preferences, and one can make a good guess just looking at […] The post Review of “Forecasting Elections” appeared first on Statistical Modeling, Causal Inference, and Social Science.
From 1992. It’s a discussion of a paper by Donoho, Johnstone, Hoch, and Stern. As I summarize: Under the “nearly black” model, the normal prior is terrible, the entropy prior is better and the exponential prior is slightly better still. (An even better prior distribution for the nearly black model would combine the threshold and […] The post Discussion of “Maximum entropy and the nearly black object” appeared first on Statistical Modeling, Causal Inference, […]
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