andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2321 knowledge-graph by maker-knowledge-mining

2321 andrew gelman stats-2014-05-05-On deck this week


meta infos for this blog

Source: html

Introduction: Mon: Can we make better graphs of global temperature history? Tues: Priors I don’t believe Wed: Cause he thinks he’s so-phisticated Thurs: Discussion with Steven Pinker on research that is attached to data that are so noisy as to be essentially uninformative Fri: Combining forecasts: Evidence on the relative accuracy of the simple average and Bayesian model averaging for predicting social science problems Sat: What property is important in a risk prediction model? Discrimination or calibration? Sun: “What should you talk about?” Plus whatever the co-bloggers want to throw in. Right now I’m super-excited about wedge sampling but I’ll let you know more about that once the paper is done.


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 Mon: Can we make better graphs of global temperature history? [sent-1, score-0.415]

2 ” Plus whatever the co-bloggers want to throw in. [sent-5, score-0.239]

3 Right now I’m super-excited about wedge sampling but I’ll let you know more about that once the paper is done. [sent-6, score-0.195]


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tfidf for this blog:

wordName wordTfidf (topN-words)

[('uninformative', 0.225), ('pinker', 0.198), ('calibration', 0.193), ('discrimination', 0.191), ('property', 0.191), ('sun', 0.191), ('fri', 0.186), ('mon', 0.182), ('tues', 0.182), ('thurs', 0.177), ('wed', 0.177), ('averaging', 0.17), ('forecasts', 0.167), ('temperature', 0.167), ('combining', 0.162), ('attached', 0.161), ('noisy', 0.158), ('sat', 0.15), ('thinks', 0.147), ('global', 0.147), ('accuracy', 0.147), ('steven', 0.144), ('predicting', 0.143), ('plus', 0.143), ('throw', 0.141), ('cause', 0.141), ('prediction', 0.131), ('relative', 0.131), ('risk', 0.128), ('priors', 0.128), ('history', 0.123), ('essentially', 0.12), ('sampling', 0.118), ('model', 0.108), ('graphs', 0.101), ('whatever', 0.098), ('average', 0.093), ('talk', 0.091), ('evidence', 0.087), ('believe', 0.086), ('simple', 0.085), ('done', 0.083), ('social', 0.078), ('let', 0.077), ('problems', 0.075), ('bayesian', 0.071), ('important', 0.069), ('discussion', 0.067), ('science', 0.066), ('ll', 0.062)]

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same-blog 1 1.0 2321 andrew gelman stats-2014-05-05-On deck this week

Introduction: Mon: Can we make better graphs of global temperature history? Tues: Priors I don’t believe Wed: Cause he thinks he’s so-phisticated Thurs: Discussion with Steven Pinker on research that is attached to data that are so noisy as to be essentially uninformative Fri: Combining forecasts: Evidence on the relative accuracy of the simple average and Bayesian model averaging for predicting social science problems Sat: What property is important in a risk prediction model? Discrimination or calibration? Sun: “What should you talk about?” Plus whatever the co-bloggers want to throw in. Right now I’m super-excited about wedge sampling but I’ll let you know more about that once the paper is done.

2 0.3834399 2320 andrew gelman stats-2014-05-05-On deck this month

Introduction: Can we make better graphs of global temperature history? Priors I don’t believe Cause he thinks he’s so-phisticated Discussion with Steven Pinker on research that is attached to data that are so noisy as to be essentially uninformative Combining forecasts: Evidence on the relative accuracy of the simple average and Bayesian model averaging for predicting social science problems What property is important in a risk prediction model? Discrimination or calibration? “What should you talk about?” Science tells us that fast food lovers are more likely to marry other fast food lovers Personally, I’d rather go with Teragram How much can we learn about individual-level causal claims from state-level correlations? Bill Easterly vs. Jeff Sachs: What percentage of the recipients didn’t use the free malaria bed nets in Zambia? Models with constraints Forum in Ecology on p-values and model selection Never back down: The culture of poverty and the culture of journalism M

3 0.27437001 2290 andrew gelman stats-2014-04-14-On deck this week

Introduction: Mon : Transitioning to Stan Tues : When you believe in things that you don’t understand Wed : Looking for Bayesian expertise in India, for the purpose of analysis of sarcoma trials Thurs : If you get to the point of asking, just do it. But some difficulties do arise . . . Fri : One-tailed or two-tailed? Sat : Index or indicator variables Sun : Fooled by randomness

4 0.26465958 2366 andrew gelman stats-2014-06-09-On deck this week

Introduction: Mon: I hate polynomials Tues: Spring forward, fall back, drop dead? Wed: Bayes in the research conversation Thurs: The health policy innovation center: how best to move from pilot studies to large-scale practice? Fri: Stroopy names Sat: He’s not so great in math but wants to do statistics and machine learning Sun: Comparing the full model to the partial model

5 0.25690219 2240 andrew gelman stats-2014-03-10-On deck this week: Things people sent me

Introduction: Mon: Preregistration: what’s in it for you? Tues: What if I were to stop publishing in journals? Wed: Empirical implications of Empirical Implications of Theoretical Models Thurs: An Economist’s Guide to Visualizing Data Fri: The maximal information coefficient Sat: Problematic interpretations of confidence intervals Sun: The more you look, the more you find

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lsi for this blog:

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Introduction: Mon: Can we make better graphs of global temperature history? Tues: Priors I don’t believe Wed: Cause he thinks he’s so-phisticated Thurs: Discussion with Steven Pinker on research that is attached to data that are so noisy as to be essentially uninformative Fri: Combining forecasts: Evidence on the relative accuracy of the simple average and Bayesian model averaging for predicting social science problems Sat: What property is important in a risk prediction model? Discrimination or calibration? Sun: “What should you talk about?” Plus whatever the co-bloggers want to throw in. Right now I’m super-excited about wedge sampling but I’ll let you know more about that once the paper is done.

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Introduction: Mon: “The results (not shown) . . .” Tues: Personally, I’d rather go with Teragram Wed: How much can we learn about individual-level causal claims from state-level correlations? Thurs: Bill Easterly vs. Jeff Sachs: What percentage of the recipients didn’t use the free malaria bed nets in Zambia? Fri: Models with constraints Sat: Forum in Ecology on p-values and model selection Sun: Never back down: The culture of poverty and the culture of journalism

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Introduction: Mon: Preregistration: what’s in it for you? Tues: What if I were to stop publishing in journals? Wed: Empirical implications of Empirical Implications of Theoretical Models Thurs: An Economist’s Guide to Visualizing Data Fri: The maximal information coefficient Sat: Problematic interpretations of confidence intervals Sun: The more you look, the more you find

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Introduction: Mon : Transitioning to Stan Tues : When you believe in things that you don’t understand Wed : Looking for Bayesian expertise in India, for the purpose of analysis of sarcoma trials Thurs : If you get to the point of asking, just do it. But some difficulties do arise . . . Fri : One-tailed or two-tailed? Sat : Index or indicator variables Sun : Fooled by randomness

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Introduction: Mon: WAIC and cross-validation in Stan! Tues: A whole fleet of gremlins: Looking more carefully at Richard Tol’s twice-corrected paper, “The Economic Effects of Climate Change” Wed: Just wondering Thurs: When you believe in things that you don’t understand Fri: I posted this as a comment on a sociology blog Sat: “Building on theories used to describe magnets, scientists have put together a model that captures something very different . . .” Sun: Why we hate stepwise regression

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lda for this blog:

topicId topicWeight

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Introduction: Mon: Can we make better graphs of global temperature history? Tues: Priors I don’t believe Wed: Cause he thinks he’s so-phisticated Thurs: Discussion with Steven Pinker on research that is attached to data that are so noisy as to be essentially uninformative Fri: Combining forecasts: Evidence on the relative accuracy of the simple average and Bayesian model averaging for predicting social science problems Sat: What property is important in a risk prediction model? Discrimination or calibration? Sun: “What should you talk about?” Plus whatever the co-bloggers want to throw in. Right now I’m super-excited about wedge sampling but I’ll let you know more about that once the paper is done.

2 0.92306769 2290 andrew gelman stats-2014-04-14-On deck this week

Introduction: Mon : Transitioning to Stan Tues : When you believe in things that you don’t understand Wed : Looking for Bayesian expertise in India, for the purpose of analysis of sarcoma trials Thurs : If you get to the point of asking, just do it. But some difficulties do arise . . . Fri : One-tailed or two-tailed? Sat : Index or indicator variables Sun : Fooled by randomness

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Introduction: This blog has roughly a month’s worth of items waiting to be posted. I post about once a day, sometimes rescheduling posts to make room for something topical. Anyway, it struck me that I know what’s coming up, but you don’t. So, here’s what we have for you during the next few days: Mon: More on US health care overkill Tues: My talks in Bristol this Wed and London this Thurs Wed: How to think about “identifiability” in Bayesian inference? Thurs: Stopping rules and Bayesian analysis Fri: The popularity of certain baby names is falling off the clifffffffffffff Plus anything our cobloggers might choose to post during these days. And, if Woody Allen or Ed Wegman or anyone else newsworthy asks us to publish an op-ed for them, we’ll consider it. Enjoy.

4 0.91590816 1897 andrew gelman stats-2013-06-13-When’s that next gamma-ray blast gonna come, already?

Introduction: Phil Plait writes : Earth May Have Been Hit by a Cosmic Blast 1200 Years Ago . . . this is nothing to panic about. If it happened at all, it was a long time ago, and unlikely to happen again for hundreds of thousands of years. This left me confused. If it really did happen 1200 years ago, basic statistics would suggest it would occur approximately once every 1200 years or so (within half an order of magnitude). So where does “hundreds of thousands of years” come from? I emailed astronomer David Hogg to see if I was missing something here, and he replied: Yeah, if we think this hit us 1200 years ago, we should imagine that this happens every few thousand years at least. Now that said, if there are *other* reasons for thinking it is exceedingly rare, then that would be a strong a priori argument against believing in the result. So you should either believe that it didn’t happen 1200 years ago, or else you should believe it will happen again in the next few thousan

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