andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-1026 knowledge-graph by maker-knowledge-mining
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Introduction: I checked and somebody went in and screwed up my fixes to the wikipedia page on Bayesian inference. I give up.
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same-blog 1 1.0 1026 andrew gelman stats-2011-11-25-Bayes wikipedia update
Introduction: I checked and somebody went in and screwed up my fixes to the wikipedia page on Bayesian inference. I give up.
2 0.25158426 767 andrew gelman stats-2011-06-15-Error in an attribution of an error
Introduction: When you say that somebody else screwed up, you have to be extra careful you’re not getting things wrong yourself! A philosopher of science is quoted as having written, “it seems best to let this grubby affair rest in a footnote,” but I think it’s good for these things to be out in the open.
3 0.17306964 2209 andrew gelman stats-2014-02-13-CmdStan, RStan, PyStan v2.2.0
Introduction: The Stan Development Team is happy to announce CmdStan, RStan, and PyStan v2.2.0. As usual, more info is available on the Stan Home Page . This is a minor release with a mix of bug fixes and features. For a full list of changes, please see the v2.2.0 milestone on stan-dev/stan’s issue tracker. Some of the bug fixes and issues are listed below. Bug Fixes increment_log_prob is now vectorized and compiles with vector arguments multinomial random number generator used the wrong size for the return value fixed memory leaks in auto-diff implementation variables can start with the prefix ‘inf’ fixed parameter output order for arrays when using optimization RStan compatibility issue with latest Rcpp 0.11.0 Features suppress command line output with refresh <= 0 added 1 to treedepth to match usual definition of treedepth added distance, squared_distance, diag_pre_multiply, diag_pre_multiply to Stan modeling lnaguage added a ‘fixed_param’ sampler for
4 0.1524965 761 andrew gelman stats-2011-06-13-A survey’s not a survey if they don’t tell you how they did it
Introduction: Since we’re on the topic of nonreplicable research . . . see here (link from here ) for a story of a survey that’s so bad that the people who did it won’t say how they did it. I know too many cases where people screwed up in a survey when they were actually trying to get the right answer, for me to trust any report of a survey that doesn’t say what they did. I’m reminded of this survey which may well have been based on a sample of size 6 (again, the people who did it refused to release any description of methodology).
5 0.1488113 640 andrew gelman stats-2011-03-31-Why Edit Wikipedia?
Introduction: Zoe Corbyn’s article for The Guardian (UK), titled Wikipedia wants more contributions from academics , and the followup discussion on Slashdot got me thinking about my own Wikipedia edits. The article quotes Dario Taraborelli, a research analyst for the Wikimedia Foundation, as saying “Academics are trapped in this paradox of using Wikipedia but not contributing,” Huh? I’m really wondering what man-in-the-street wrote all the great stats stuff out there. And what’s the paradox? I use lots of things without contributing to them. Taraborelli is further quoted as saying “The Wikimedia Foundation is looking at how it might capture expert conversation about Wikipedia content happening on other websites and feed it back to the community as a way of providing pointers for improvement.” This struck home. I recently went through the entry for latent Dirichlet allocation and found a bug in their derivation. I wrote up a revised derivation and posted it on my own blog .
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Introduction: I checked and somebody went in and screwed up my fixes to the wikipedia page on Bayesian inference. I give up.
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Introduction: Hykel Hosni noticed this bit from the Lindley Prize page of the Society for Bayesan Analysis: Lindley became a great missionary for the Bayesian gospel. The atmosphere of the Bayesian revival is captured in a comment by Rivett on Lindley’s move to University College London and the premier chair of statistics in Britain: “it was as though a Jehovah’s Witness had been elected Pope.” From my perspective, this was amusing (if commonplace): a group of rationalists jocularly characterizing themselves as religious fanatics. And some of this is in response to intense opposition from outsiders (see the Background section here ). That’s my view. I’m an insider, a statistician who’s heard all jokes about religious Bayesians, from Bayesian and non-Bayesian statisticians alike. But Hosni is an outsider, and here’s how he sees the above-quoted paragraph: Research, however, is not a matter of faith but a matter of arguments, which should always be evaluated with the utmost intellec
3 0.60346615 1067 andrew gelman stats-2011-12-18-Christopher Hitchens was a Bayesian
Introduction: 1. We Bayesian statisticians like to say there are three kinds of statisticians: a. Bayesians; b. People who are Bayesians but don’t realize it (that is, they act in coherence with some unstated probability); c. Failed Bayesians (that is, people whose inference could be improved by some attention to coherence). So, if a statistician does great work, we are inclined to claim this person for the Bayesian cause, even if he or she vehemently denies any Bayesian leanings. 2. In his autobiography, Bertrand Russell tells the story of when he went to prison for opposing World War 1: I [Russell] was much cheered on my arrival by the warden at the gate, who had to take particulars about me. He asked my religion, and I replied ‘agnostic.’ He asked how to spell it, and remarked with a sigh: “Well, there are many religions, but I suppose they all worship the same God.” This remark kept me cheerful for about a week. 3. In an op-ed today, Ross Douthat argues that celebrated a
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Introduction: Loren Maxwell writes: I am trying to do some studies on the PageRank algorithm with applying a Bayesian technique. If you are not familiar with PageRank, it is the basis for how Google ranks their pages. It basically treats the internet as a large social network with each link conferring some value onto the page it links to. For example, if I had a webpage that had only one link to it, say from my friend’s webpage, then its PageRank would be dependent on my friend’s PageRank, presumably quite low. However, if the one link to my page was off the Google search page, then my PageRank would be quite high since there are undoubtedly millions of pages linking to Google and few pages that Google links to. The end result of the algorithm, however, is that all the PageRank values of the nodes in the network sum to one and the PageRank of a specific node is the probability that a “random surfer” will end up on that node. For example, in the attached spreadsheet, Column D shows e
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Introduction: My paper with Christian Robert, “Not Only Defended But Also Applied”: The Perceived Absurdity of Bayesian Inference , was recently published in The American Statistician, along with discussions by Steve Fienberg, Steve Stigler, Deborah Mayo, and Wesley Johnson, and our rejoinder, The Anti-Bayesian Moment and Its Passing . These articles revolved around the question of why the great probabilist William Feller, in his classic book on probability (“Feller, Volume 1,” as it is known), was so intemperately anti-Bayesian. We located Feller’s attitude within a post-WW2 “anti-Bayesian moment” in which Bayesian inference was perceived as a threat to the dominance of non-Bayesian methods, which were mature enough to have solved problems yet new enough to still appear to have limitless promise. Howard Wainer read this. Howard is a friend who has a longstanding interest in the history of statistics and who also has known a lot of important statisticians over the years. Howard writes: O
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Introduction: Malecki asks: Is this the worst infographic ever to appear in NYT? USA Today is not something to aspire to. To connect to some of our recent themes , I agree this is a pretty horrible data display. But it’s not bad as a series of images. Considering the competition to be a cartoon or series of photos, these images aren’t so bad. One issue, I think, is that designers get credit for creativity and originality (unusual color combinations! Histogram bars shaped like mosques!) , which is often the opposite of what we want in a clear graph. It’s Martin Amis vs. George Orwell all over again.
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Introduction: “A statistical model is usually taken to be summarized by a likelihood, or a likelihood and a prior distribution, but we go an extra step by noting that the parameters of a model are typically batched, and we take this batching as an essential part of the model.”
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