andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-1091 knowledge-graph by maker-knowledge-mining
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Introduction: David Schminovich points me to this paper by Yu Lu, H. Mo, Martin Weinberg, and Neal Katz: We believe that a wide range of physical processes conspire to shape the observed galaxy population but we remain unsure of their detailed interactions. The semi-analytic model (SAM) of galaxy formation uses multi-dimensional parameterisations of the physical processes of galaxy formation and provides a tool to constrain these underlying physical interactions. Because of the high dimensionality, the parametric problem of galaxy formation may be profitably tackled with a Bayesian-inference based approach, which allows one to constrain theory with data in a statistically rigorous way. In this paper we develop a SAM in the framework of Bayesian inference. . . . And here’s another from the same authors, this time on “Bayesian inference of galaxy formation from the K-band luminosity function of galaxies: tensions between theory and observation.” I haven’t actually looked at the papers but
sentIndex sentText sentNum sentScore
1 David Schminovich points me to this paper by Yu Lu, H. [sent-1, score-0.05]
2 Mo, Martin Weinberg, and Neal Katz: We believe that a wide range of physical processes conspire to shape the observed galaxy population but we remain unsure of their detailed interactions. [sent-2, score-1.556]
3 The semi-analytic model (SAM) of galaxy formation uses multi-dimensional parameterisations of the physical processes of galaxy formation and provides a tool to constrain these underlying physical interactions. [sent-3, score-3.157]
4 Because of the high dimensionality, the parametric problem of galaxy formation may be profitably tackled with a Bayesian-inference based approach, which allows one to constrain theory with data in a statistically rigorous way. [sent-4, score-1.968]
5 In this paper we develop a SAM in the framework of Bayesian inference. [sent-5, score-0.175]
6 And here’s another from the same authors, this time on “Bayesian inference of galaxy formation from the K-band luminosity function of galaxies: tensions between theory and observation. [sent-9, score-1.398]
7 ” I haven’t actually looked at the papers but I thought some of you out there might be interested. [sent-10, score-0.09]
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Introduction: David Schminovich points me to this paper by Yu Lu, H. Mo, Martin Weinberg, and Neal Katz: We believe that a wide range of physical processes conspire to shape the observed galaxy population but we remain unsure of their detailed interactions. The semi-analytic model (SAM) of galaxy formation uses multi-dimensional parameterisations of the physical processes of galaxy formation and provides a tool to constrain these underlying physical interactions. Because of the high dimensionality, the parametric problem of galaxy formation may be profitably tackled with a Bayesian-inference based approach, which allows one to constrain theory with data in a statistically rigorous way. In this paper we develop a SAM in the framework of Bayesian inference. . . . And here’s another from the same authors, this time on “Bayesian inference of galaxy formation from the K-band luminosity function of galaxies: tensions between theory and observation.” I haven’t actually looked at the papers but
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Introduction: Rob Kass’s article on statistical pragmatism is scheduled to appear in Statistical Science along with some discussions. Here are my comments. I agree with Rob Kass’s point that we can and should make use of statistical methods developed under different philosophies, and I am happy to take the opportunity to elaborate on some of his arguments. I’ll discuss the following: - Foundations of probability - Confidence intervals and hypothesis tests - Sampling - Subjectivity and belief - Different schools of statistics Foundations of probability. Kass describes probability theory as anchored upon physical randomization (coin flips, die rolls and the like) but being useful more generally as a mathematical model. I completely agree but would also add another anchoring point: calibration. Calibration of probability assessments is an objective, not subjective process, although some subjectivity (or scientific judgment) is necessarily involved in the choice of events used
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Introduction: David Schminovich points me to this paper by Yu Lu, H. Mo, Martin Weinberg, and Neal Katz: We believe that a wide range of physical processes conspire to shape the observed galaxy population but we remain unsure of their detailed interactions. The semi-analytic model (SAM) of galaxy formation uses multi-dimensional parameterisations of the physical processes of galaxy formation and provides a tool to constrain these underlying physical interactions. Because of the high dimensionality, the parametric problem of galaxy formation may be profitably tackled with a Bayesian-inference based approach, which allows one to constrain theory with data in a statistically rigorous way. In this paper we develop a SAM in the framework of Bayesian inference. . . . And here’s another from the same authors, this time on “Bayesian inference of galaxy formation from the K-band luminosity function of galaxies: tensions between theory and observation.” I haven’t actually looked at the papers but
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1 0.89707577 1738 andrew gelman stats-2013-02-25-Plaig
Introduction: “‘The distortion of a text,’ says Freud in Moses and Monotheism, ‘is not unlike a murder. The difficulty lies not in the execution of the deed but in doing away with the traces.’” — James Wood, in The Fun Stuff (2012).
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Introduction: David Schminovich points me to this paper by Yu Lu, H. Mo, Martin Weinberg, and Neal Katz: We believe that a wide range of physical processes conspire to shape the observed galaxy population but we remain unsure of their detailed interactions. The semi-analytic model (SAM) of galaxy formation uses multi-dimensional parameterisations of the physical processes of galaxy formation and provides a tool to constrain these underlying physical interactions. Because of the high dimensionality, the parametric problem of galaxy formation may be profitably tackled with a Bayesian-inference based approach, which allows one to constrain theory with data in a statistically rigorous way. In this paper we develop a SAM in the framework of Bayesian inference. . . . And here’s another from the same authors, this time on “Bayesian inference of galaxy formation from the K-band luminosity function of galaxies: tensions between theory and observation.” I haven’t actually looked at the papers but
3 0.72833616 952 andrew gelman stats-2011-10-11-More reason to like Sims besides just his name
Introduction: John Horton points to Sims ‘s comment on Angrist and Pischke : Top of page 8—he criticizes economists for using clustered standard errors—suggests using multilevel models instead. Awesome! So now there are at least two Nobel prize winners in economics who’ve expressed skepticism about controlled experiments. (I wonder if Sims is such a danger in a parking lot.) P.S. I’m still miffed that this journal didn’t invite me to comment on that article!
Introduction: Ole Rogeberg writes: Recently read your blogpost on Pinker’s views regarding red and blue states . This might help you see where he’s coming from: The “conflict of visions” thing that Pinker repeats to likely refers to Thomas Sowell’s work in the books “Conflict of Visions” and “Visions of the anointed.” The “Conflict of visions” book is on his top-5 favorite book list and in a Q&A; interview he explains it as follows: Q: What is the Tragic Vision vs. the Utopian Vision? A: They are the different visions of human nature that underlie left-wing and right-wing ideologies. The distinction comes from the economist Thomas Sowell in his wonderful book “A Conflict of Visions.” According to the Tragic Vision, humans are inherently limited in virtue, wisdom, and knowledge, and social arrangements must acknowledge those limits. According to the Utopian vision, these limits are “products†of our social arrangements, and we should strive to overcome them in a better society of the f
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