andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-227 knowledge-graph by maker-knowledge-mining

227 andrew gelman stats-2010-08-23-Visualization magazine


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Introduction: Aleks pointed me to this .


Summary: the most important sentenses genereted by tfidf model

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tfidf for this blog:

wordName wordTfidf (topN-words)

[('aleks', 0.831), ('pointed', 0.556)]

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same-blog 1 1.0 227 andrew gelman stats-2010-08-23-Visualization magazine

Introduction: Aleks pointed me to this .

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Introduction: Aleks sent me this (I’m not sure from where):

3 0.51131076 281 andrew gelman stats-2010-09-16-NSF crowdsourcing

Introduction: I have no idea what this and this are, but Aleks passed these on, and maybe some of you will find them interesting.

4 0.50601643 909 andrew gelman stats-2011-09-15-7 steps to successful infographics

Introduction: Aleks pointed me to this interesting article by Sarah Slobin. I don’t have any good way of summarizing it, so you’ll just have to follow the link and read it.

5 0.48464799 1302 andrew gelman stats-2012-05-06-Fun with google autocomplete

Introduction: Aleks points us to this idea of labeling for news.

6 0.40900347 441 andrew gelman stats-2010-12-01-Mapmaking software

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same-blog 1 0.99973631 227 andrew gelman stats-2010-08-23-Visualization magazine

Introduction: Aleks pointed me to this .

2 0.98664469 1063 andrew gelman stats-2011-12-16-Suspicious histogram bars

Introduction: Aleks sent me this (I’m not sure from where):

3 0.96982318 281 andrew gelman stats-2010-09-16-NSF crowdsourcing

Introduction: I have no idea what this and this are, but Aleks passed these on, and maybe some of you will find them interesting.

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Introduction: Aleks points us to this idea of labeling for news.

5 0.88389218 1064 andrew gelman stats-2011-12-16-The benefit of the continuous color scale

Introduction: Aleks sends in this item from Gabriel Florit on choropleth classification systems. Oddly enough, Malecki and I were just talking about color mapping schemes earlier today.

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14 0.58358186 455 andrew gelman stats-2010-12-07-Some ideas on communicating risks to the general public

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same-blog 1 0.98147273 227 andrew gelman stats-2010-08-23-Visualization magazine

Introduction: Aleks pointed me to this .

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Introduction: The list includes “hunting” but not “fishing,” so that’s cool. I wonder how they’d feel about a question involving different cuts of meat. In any case, I’m happy to see that “Bayes” is not on the banned list. P.S. Russell explains .

3 0.39849785 1494 andrew gelman stats-2012-09-13-Watching the sharks jump

Introduction: Recently in the sister blog: Niall Ferguson is a hack . Niall Ferguson is not always a hack, sometimes he just makes silly mistakes . Paul Krugman is not a hack, but he sometimes he goes over the top . Reflections on hacks . P.S. Yes, technically I’m misusing the expression, it should really be something like, “Watching the sharks get jumped.” But I liked the image of the jumping shark.

4 0.39348823 672 andrew gelman stats-2011-04-20-The R code for those time-use graphs

Introduction: By popular demand, here’s my R script for the time-use graphs : # The data a1 <- c(4.2,3.2,11.1,1.3,2.2,2.0) a2 <- c(3.9,3.2,10.0,0.8,3.1,3.1) a3 <- c(6.3,2.5,9.8,0.9,2.2,2.4) a4 <- c(4.4,3.1,9.8,0.8,3.3,2.7) a5 <- c(4.8,3.0,9.9,0.7,3.3,2.4) a6 <- c(4.0,3.4,10.5,0.7,3.3,2.1) a <- rbind(a1,a2,a3,a4,a5,a6) avg <- colMeans (a) avg.array <- t (array (avg, rev(dim(a)))) diff <- a - avg.array country.name <- c("France", "Germany", "Japan", "Britain", "USA", "Turkey") # The line plots par (mfrow=c(2,3), mar=c(4,4,2,.5), mgp=c(2,.7,0), tck=-.02, oma=c(3,0,4,0), bg="gray96", fg="gray30") for (i in 1:6){ plot (c(1,6), c(-1,1.7), xlab="", ylab="", xaxt="n", yaxt="n", bty="l", type="n") lines (1:6, diff[i,], col="blue") points (1:6, diff[i,], pch=19, col="black") if (i>3){ axis (1, c(1,3,5), c ("Work,\nstudy", "Eat,\nsleep", "Leisure"), mgp=c(2,1.5,0), tck=0, cex.axis=1.2) axis (1, c(2,4,6), c ("Unpaid\nwork", "Personal\nCare", "Other"), mgp=c(2,1.5,0),

5 0.3837623 964 andrew gelman stats-2011-10-19-An interweaving-transformation strategy for boosting MCMC efficiency

Introduction: Yaming Yu and Xiao-Li Meng write in with a cool new idea for improving the efficiency of Gibbs and Metropolis in multilevel models: For a broad class of multilevel models, there exist two well-known competing parameterizations, the centered parameterization (CP) and the non-centered parameterization (NCP), for effective MCMC implementation. Much literature has been devoted to the questions of when to use which and how to compromise between them via partial CP/NCP. This article introduces an alternative strategy for boosting MCMC efficiency via simply interweaving—but not alternating—the two parameterizations. This strategy has the surprising property that failure of both the CP and NCP chains to converge geometrically does not prevent the interweaving algorithm from doing so. It achieves this seemingly magical property by taking advantage of the discordance of the two parameterizations, namely, the sufficiency of CP and the ancillarity of NCP, to substantially reduce the Markovian

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