andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-1091 knowledge-graph by maker-knowledge-mining

1091 andrew gelman stats-2011-12-29-Bayes in astronomy


meta infos for this blog

Source: html

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


Summary: the most important sentenses genereted by tfidf model

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]


similar blogs computed by tfidf model

tfidf for this blog:

wordName wordTfidf (topN-words)

[('galaxy', 0.656), ('formation', 0.451), ('constrain', 0.208), ('physical', 0.185), ('sam', 0.16), ('processes', 0.134), ('galaxies', 0.12), ('tensions', 0.12), ('tackled', 0.113), ('profitably', 0.113), ('lu', 0.113), ('unsure', 0.108), ('mo', 0.104), ('dimensionality', 0.104), ('katz', 0.104), ('yu', 0.104), ('neal', 0.091), ('parametric', 0.088), ('theory', 0.081), ('shape', 0.081), ('martin', 0.076), ('rigorous', 0.076), ('detailed', 0.069), ('wide', 0.068), ('allows', 0.065), ('develop', 0.064), ('bayesian', 0.062), ('tool', 0.062), ('remain', 0.061), ('framework', 0.061), ('provides', 0.058), ('uses', 0.057), ('range', 0.056), ('observed', 0.056), ('underlying', 0.054), ('function', 0.052), ('paper', 0.05), ('statistically', 0.05), ('looked', 0.049), ('haven', 0.048), ('population', 0.045), ('authors', 0.044), ('david', 0.044), ('papers', 0.041), ('approach', 0.038), ('inference', 0.038), ('believe', 0.037), ('high', 0.035), ('interested', 0.035), ('may', 0.032)]

similar blogs list:

simIndex simValue blogId blogTitle

same-blog 1 1.0 1091 andrew gelman stats-2011-12-29-Bayes in astronomy

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

2 0.23514599 332 andrew gelman stats-2010-10-10-Proposed new section of the American Statistical Association on Imaging Sciences

Introduction: Martin Lindquist writes that he and others are trying to start a new ASA section on statistics in imaging. If you’re interested in being a signatory to its formation, please send him an email.

3 0.089944147 994 andrew gelman stats-2011-11-06-Josh Tenenbaum presents . . . a model of folk physics!

Introduction: Josh Tenenbaum describes some new work modeling people’s physical reasoning as probabilistic inferences over intuitive theories of mechanics. A general-purpose capacity for “physical intelligence”—inferring physical properties of objects and predicting future states in complex dynamical scenes—is central to how humans interpret their environment and plan safe and effective actions. The computations and representations underlying physical intelligence remain unclear, however. Cognitive studies have focused on mapping out judgment biases and errors, or on testing simple heuristic models suitable only for highly specific cases; they have not attempted to give general-purpose unifying models. In computer science, artificial intelligence and robotics researchers have long sought to formalize common-sense physical reasoning but without success in approaching human-level competence. Here we show that a wide range of human physical judgments can be explained by positing an “intuitive me

4 0.073364735 2347 andrew gelman stats-2014-05-25-Why I decided not to be a physicist

Introduction: As I’ve written before, I was a math and physics major in college but I switched to statistics because math seemed pointless if you weren’t the best (and I knew there were people better than me), and I just didn’t feel like I had a good physical understanding. My lack of physical understanding comes up from time to time. An example occurred the other day. I was viewing a demonstration of Foucault’s pendulum and the guide said the period was something like 35 hours. I was surprised, having always thought it had a 24-hour period. Sure, I can understand it in words, but, even after reflecting on it for a minute, I couldn’t see my way to even an approximate derivation. If I wanted to, I think I could go through the math (for example here ), but I feel that if I really had the intuition, I wouldn’t need to. I’m sure I have a lot more physical understanding than the average person but I don’t think I have enough to be a real physicist.

5 0.053854175 662 andrew gelman stats-2011-04-15-Bayesian statistical pragmatism

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

6 0.053568374 2276 andrew gelman stats-2014-03-31-On deck this week

7 0.0529783 1205 andrew gelman stats-2012-03-09-Coming to agreement on philosophy of statistics

8 0.051288545 1794 andrew gelman stats-2013-04-09-My talks in DC and Baltimore this week

9 0.049176861 317 andrew gelman stats-2010-10-04-Rob Kass on statistical pragmatism, and my reactions

10 0.048464973 1829 andrew gelman stats-2013-04-28-Plain old everyday Bayesianism!

11 0.046939202 1469 andrew gelman stats-2012-08-25-Ways of knowing

12 0.046051227 1185 andrew gelman stats-2012-02-26-A statistician’s rants and raves

13 0.044887938 781 andrew gelman stats-2011-06-28-The holes in my philosophy of Bayesian data analysis

14 0.043860361 1554 andrew gelman stats-2012-10-31-It not necessary that Bayesian methods conform to the likelihood principle

15 0.043851346 2357 andrew gelman stats-2014-06-02-Why we hate stepwise regression

16 0.043367207 316 andrew gelman stats-2010-10-03-Suggested reading for a prospective statistician?

17 0.042840306 972 andrew gelman stats-2011-10-25-How do you interpret standard errors from a regression fit to the entire population?

18 0.042805851 1652 andrew gelman stats-2013-01-03-“The Case for Inductive Theory Building”

19 0.042792995 259 andrew gelman stats-2010-09-06-Inbox zero. Really.

20 0.04219221 1418 andrew gelman stats-2012-07-16-Long discussion about causal inference and the use of hierarchical models to bridge between different inferential settings


similar blogs computed by lsi model

lsi for this blog:

topicId topicWeight

[(0, 0.065), (1, 0.043), (2, -0.023), (3, -0.012), (4, -0.023), (5, 0.001), (6, -0.024), (7, -0.005), (8, 0.011), (9, -0.014), (10, 0.002), (11, 0.002), (12, -0.009), (13, -0.002), (14, 0.005), (15, 0.025), (16, 0.024), (17, -0.0), (18, -0.012), (19, -0.001), (20, -0.001), (21, 0.011), (22, -0.008), (23, -0.015), (24, 0.021), (25, 0.006), (26, 0.008), (27, 0.007), (28, 0.006), (29, 0.002), (30, 0.004), (31, -0.006), (32, -0.003), (33, -0.011), (34, -0.02), (35, -0.008), (36, -0.008), (37, -0.009), (38, 0.001), (39, 0.022), (40, 0.01), (41, 0.012), (42, -0.001), (43, -0.041), (44, -0.013), (45, -0.036), (46, 0.016), (47, 0.004), (48, 0.006), (49, -0.002)]

similar blogs list:

simIndex simValue blogId blogTitle

same-blog 1 0.94787377 1091 andrew gelman stats-2011-12-29-Bayes in astronomy

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

2 0.73447114 1469 andrew gelman stats-2012-08-25-Ways of knowing

Introduction: In this discussion from last month, computer science student and Judea Pearl collaborator Elias Barenboim expressed an attitude that hierarchical Bayesian methods might be fine in practice but that they lack theory, that Bayesians can’t succeed in toy problems. I posted a P.S. there which might not have been noticed so I will put it here: I now realize that there is some disagreement about what constitutes a “guarantee.” In one of his comments, Barenboim writes, “the assurance we have that the result must hold as long as the assumptions in the model are correct should be regarded as a guarantee.” In that sense, yes, we have guarantees! It is fundamental to Bayesian inference that the result must hold if the assumptions in the model are correct. We have lots of that in Bayesian Data Analysis (particularly in the first four chapters but implicitly elsewhere as well), and this is also covered in the classic books by Lindley, Jaynes, and others. This sort of guarantee is indeed p

3 0.71482027 1443 andrew gelman stats-2012-08-04-Bayesian Learning via Stochastic Gradient Langevin Dynamics

Introduction: Burak Bayramli writes: In this paper by Sunjin Ahn, Anoop Korattikara, and Max Welling and this paper by Welling and Yee Whye The, there are some arguments on big data and the use of MCMC. Both papers have suggested improvements to speed up MCMC computations. I was wondering what your thoughts were, especially on this paragraph: When a dataset has a billion data-cases (as is not uncommon these days) MCMC algorithms will not even have generated a single (burn-in) sample when a clever learning algorithm based on stochastic gradients may already be making fairly good predictions. In fact, the intriguing results of Bottou and Bousquet (2008) seem to indicate that in terms of “number of bits learned per unit of computation”, an algorithm as simple as stochastic gradient descent is almost optimally efficient. We therefore argue that for Bayesian methods to remain useful in an age when the datasets grow at an exponential rate, they need to embrace the ideas of the stochastic optimiz

4 0.70463705 1829 andrew gelman stats-2013-04-28-Plain old everyday Bayesianism!

Introduction: Sam Behseta writes: There is a report by Martin Tingley and Peter Huybers in Nature on the unprecedented high temperatures at northern latitudes (Russia, Greenland, etc). What is more interesting is the authors are have used a straightforward hierarchical Bayes model, and for the first time (as far as I can remember) the results are reported with a probability attached to them (P>0.99), as opposed to the usual p-value<0.01 business. This might be a sign that editors of big time science journals are welcoming Bayesian approaches. I agree. This is a good sign for statistical communication. Here are the key sentences from the abstract: Here, using a hierarchical Bayesian analysis of instrumental, tree-ring, ice-core and lake-sediment records, we show that the magnitude and frequency of recent warm temperature extremes at high northern latitudes are unprecedented in the past 600 years. The summers of 2005, 2007, 2010 and 2011 were warmer than those of all prior years back to 1

5 0.67705214 2368 andrew gelman stats-2014-06-11-Bayes in the research conversation

Introduction: Charlie Williams writes: As I get interested in Bayesian approaches to statistics, I have one question I wondered if you would find interesting to address at some point on the blog. What does Bayesian work look like in action across a field? From experience, I have some feeling for how ongoing debates evolve (or not) with subsequent studies in response to earlier findings. I wonder if you know how this happens in practice when multiple researchers are using Bayesian approaches. How much are previous findings built into priors? How much advance comes from model improvement? And in a social science field where self-selection and self-interest play a role, how are improved “treatment” effects incorporated and evaluated? I thought you might know of a field where actual back and forth has been carried out mostly in the context of Bayesian analysis or inference, and I thought it would be interesting to take a look at an example as I think about my own field. My reply: I’ve seen Ba

6 0.67363644 1571 andrew gelman stats-2012-11-09-The anti-Bayesian moment and its passing

7 0.66916144 566 andrew gelman stats-2011-02-09-The boxer, the wrestler, and the coin flip, again

8 0.6688394 291 andrew gelman stats-2010-09-22-Philosophy of Bayes and non-Bayes: A dialogue with Deborah Mayo

9 0.66069561 1280 andrew gelman stats-2012-04-24-Non-Bayesian analysis of Bayesian agents?

10 0.65850633 1182 andrew gelman stats-2012-02-24-Untangling the Jeffreys-Lindley paradox

11 0.65702188 1610 andrew gelman stats-2012-12-06-Yes, checking calibration of probability forecasts is part of Bayesian statistics

12 0.65186012 1868 andrew gelman stats-2013-05-23-Validation of Software for Bayesian Models Using Posterior Quantiles

13 0.65147936 1779 andrew gelman stats-2013-03-27-“Two Dogmas of Strong Objective Bayesianism”

14 0.65046608 114 andrew gelman stats-2010-06-28-More on Bayesian deduction-induction

15 0.65010452 1156 andrew gelman stats-2012-02-06-Bayesian model-building by pure thought: Some principles and examples

16 0.64942104 1438 andrew gelman stats-2012-07-31-What is a Bayesian?

17 0.64795417 1205 andrew gelman stats-2012-03-09-Coming to agreement on philosophy of statistics

18 0.64560479 1262 andrew gelman stats-2012-04-12-“Not only defended but also applied”: The perceived absurdity of Bayesian inference

19 0.64332658 2349 andrew gelman stats-2014-05-26-WAIC and cross-validation in Stan!

20 0.64212972 1095 andrew gelman stats-2012-01-01-Martin and Liu: Probabilistic inference based on consistency of model with data


similar blogs computed by lda model

lda for this blog:

topicId topicWeight

[(2, 0.015), (11, 0.019), (15, 0.041), (16, 0.014), (24, 0.12), (29, 0.017), (49, 0.376), (54, 0.02), (77, 0.013), (95, 0.029), (99, 0.191)]

similar blogs list:

simIndex simValue blogId blogTitle

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).

same-blog 2 0.8556999 1091 andrew gelman stats-2011-12-29-Bayes in astronomy

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!

4 0.71121681 1892 andrew gelman stats-2013-06-10-I don’t think we get much out of framing politics as the Tragic Vision vs. the Utopian Vision

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

5 0.70999217 1709 andrew gelman stats-2013-02-06-The fractal nature of scientific revolutions

Introduction: Phil Earnhardt writes: I stumbled across your blog entry after googling on those terms. If I could comment on the closed entry [We had to shut off comments on old blog entries for reasons of spam --- ed.], I’d note: scientific revolutions are fractal; they’re also chaotic in their dynamics. Predictability when a particular scientific revolution will take hold—or be rejected—is problematic. I find myself wishing that Chaos Theory had been established when Kuhn wrote his essay.

6 0.70163679 1969 andrew gelman stats-2013-08-05-New issue of Symposium magazine

7 0.66516244 354 andrew gelman stats-2010-10-19-There’s only one Amtrak

8 0.65912902 570 andrew gelman stats-2011-02-12-Software request

9 0.65322089 634 andrew gelman stats-2011-03-29-A.I. is Whatever We Can’t Yet Automate

10 0.64966702 812 andrew gelman stats-2011-07-21-Confusion about “rigging the numbers,” the support of ideological opposites, who’s a 501(c)(3), and the asymmetry of media bias

11 0.62083083 792 andrew gelman stats-2011-07-08-The virtues of incoherence?

12 0.61275977 1007 andrew gelman stats-2011-11-13-At last, treated with the disrespect that I deserve

13 0.59418958 2044 andrew gelman stats-2013-09-30-Query from a textbook author – looking for stories to tell to undergrads about significance

14 0.58654296 2060 andrew gelman stats-2013-10-13-New issue of Symposium magazine

15 0.5813188 828 andrew gelman stats-2011-07-28-Thoughts on Groseclose book on media bias

16 0.57667065 612 andrew gelman stats-2011-03-14-Uh-oh

17 0.57341921 2338 andrew gelman stats-2014-05-19-My short career as a Freud expert

18 0.56130278 1560 andrew gelman stats-2012-11-03-Statistical methods that work in some settings but not others

19 0.54823065 806 andrew gelman stats-2011-07-17-6 links

20 0.53906459 2161 andrew gelman stats-2014-01-07-My recent debugging experience