andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1799 knowledge-graph by maker-knowledge-mining
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Introduction: The Stan Development Team is happy to announce that Stan 1.3.0 and RStan 1.3.0 are available for download. Follow the links on: Stan home page: http://mc-stan.org/ Please let us know if you have problems updating. Here’s the full set of release notes. v1.3.0 (12 April 2013) ====================================================================== Enhancements ---------------------------------- Modeling Language * forward sampling (random draws from distributions) in generated quantities * better error messages in parser * new distributions: + exp_mod_normal + gumbel + skew_normal * new special functions: + owenst * new broadcast (repetition) functions for vectors, arrays, matrices + rep_arrray + rep_matrix + rep_row_vector + rep_vector Command-Line * added option to display autocorrelations in the command-line program to print output * changed default point estimation routine from the command line to
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1 * typos in the manual * rstan: + fixed crash in R when index is out of bounds using set_cppo("fast") + io_context fix skipping len=0 + fix the typo in manual (dims -> dim) + add require(inline) to fix the problem with loading sysdata. [sent-18, score-1.255]
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Introduction: We’re happy to announce the availability of Stan and RStan versions 1.1.0, which are general tools for performing model-based Bayesian inference using the no-U-turn sampler, an adaptive form of Hamiltonian Monte Carlo. Information on downloading and installing and using them is available as always from Stan Home Page: http://mc-stan.org/ Let us know if you have any problems on the mailing lists or at the e-mails linked on the home page (please don’t use this web page). The full release notes follow. (R)Stan Version 1.1.0 Release Notes =================================== -- Backward Compatibility Issue * Categorical distribution recoded to match documentation; it now has support {1,...,K} rather than {0,...,K-1}. * (RStan) change default value of permuted flag from FALSE to TRUE for Stan fit S4 extract() method -- New Features * Conditional (if-then-else) statements * While statements -- New Functions * generalized multiply_lower_tri
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Introduction: The Stan Development Team is happy to announce that Stan 1.3.0 and RStan 1.3.0 are available for download. Follow the links on: Stan home page: http://mc-stan.org/ Please let us know if you have problems updating. Here’s the full set of release notes. v1.3.0 (12 April 2013) ====================================================================== Enhancements ---------------------------------- Modeling Language * forward sampling (random draws from distributions) in generated quantities * better error messages in parser * new distributions: + exp_mod_normal + gumbel + skew_normal * new special functions: + owenst * new broadcast (repetition) functions for vectors, arrays, matrices + rep_arrray + rep_matrix + rep_row_vector + rep_vector Command-Line * added option to display autocorrelations in the command-line program to print output * changed default point estimation routine from the command line to
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Introduction: We’re happy to announce the availability of Stan and RStan versions 1.1.0, which are general tools for performing model-based Bayesian inference using the no-U-turn sampler, an adaptive form of Hamiltonian Monte Carlo. Information on downloading and installing and using them is available as always from Stan Home Page: http://mc-stan.org/ Let us know if you have any problems on the mailing lists or at the e-mails linked on the home page (please don’t use this web page). The full release notes follow. (R)Stan Version 1.1.0 Release Notes =================================== -- Backward Compatibility Issue * Categorical distribution recoded to match documentation; it now has support {1,...,K} rather than {0,...,K-1}. * (RStan) change default value of permuted flag from FALSE to TRUE for Stan fit S4 extract() method -- New Features * Conditional (if-then-else) statements * While statements -- New Functions * generalized multiply_lower_tri
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Introduction: The Stan Development Team is happy to announce that Stan 1.3.0 and RStan 1.3.0 are available for download. Follow the links on: Stan home page: http://mc-stan.org/ Please let us know if you have problems updating. Here’s the full set of release notes. v1.3.0 (12 April 2013) ====================================================================== Enhancements ---------------------------------- Modeling Language * forward sampling (random draws from distributions) in generated quantities * better error messages in parser * new distributions: + exp_mod_normal + gumbel + skew_normal * new special functions: + owenst * new broadcast (repetition) functions for vectors, arrays, matrices + rep_arrray + rep_matrix + rep_row_vector + rep_vector Command-Line * added option to display autocorrelations in the command-line program to print output * changed default point estimation routine from the command line to
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Introduction: Frank Hansen updates his story and writes: Here is a link to the new stuff. The update is a little less than half way down the page. 1. used display() instead of summary() 2. include a proxy for [non] newbies — whether I can find their name in a previous Chicago Marathon. 3. graph actual pace vs. fitted pace (color code newbie proxy) 4. estimate the model separately for newbies and non-newbies. some incidental discussion of sd of errors. There are a few things unfinished but I have to get to bed, I’m running the 2010 Chicago Half tomorrow morning, and they moved the start up from 7:30 to 7:00 because it’s the day of the Bears home opener too.
Introduction: I know next to nothing about golf. My mini-golf scores typically approach the maximum of 7 per hole, and I’ve never actually played macro-golf. I did publish a paper on golf once ( A Probability Model for Golf Putting , with Deb Nolan), but it’s not so rare for people to publish papers on topics they know nothing about. Those who can’t, research. But I certainly have the ability to post other people’s ideas. Charles Murray writes: I [Murray] am playing around with the likelihood of Tiger Woods breaking Nicklaus’s record in the Majors. I’ve already gone on record two years ago with the reason why he won’t, but now I’m looking at it from a non-psychological perspective. Given the history of the majors, what how far above the average _for other great golfers_ does Tiger have to perform? Here’s the procedure I’ve been working on: 1. For all golfers who have won at at least one major since 1934 (the year the Masters began), create 120 lines: one for each Major for each year f
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Introduction: A friend asks the above question and writes: This article left me thinking – how could the IRS not notice that this guy didn’t file taxes for several years? Don’t they run checks and notice if you miss a year? If I write a check our of order, there’s an asterisk next to the check number in my next bank statement showing that there was a gap in the sequence. If you ran the IRS, wouldn’t you do this: SSNs are issued sequentially. Once a SSN reaches 18, expect it to file a return. If it doesn’t, mail out a postage paid letter asking why not with check boxes such as Student, Unemployed, etc. Follow up at reasonable intervals. Eventually every SSN should be filing a return, or have an international address. Yes this is intrusive, but my goal is only to maximize tax revenue. Surely people who do this for a living could come up with something more elegant. My response: I dunno, maybe some confidentiality rules? The other thing is that I’m guessing that IRS gets lots of pushback w
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Introduction: Todd Schneider writes: Apropos of your recent blog post about modeling score differential of basketball games , I thought you might enjoy a site I built, gambletron2000.com , that gathers real-time win probabilities from betting markets for most major sports (including NBA and college basketball). My original goal was to use the variance of changes in win probabilities to quantify which games were the most exciting, but I got a bit carried away and ended up pursuing a bunch of other ideas, which you can read about in the full writeup here This particular passage from the anonymous someone in your post: My idea is for each timestep in a game (a second, 5 seconds, etc), use the Vegas line, the current score differential, who has the ball, and the number of possessions played already (to account for differences in pace) to create a point estimate probability of the home team winning. reminded me of a graph I made, which shows the mean-reverting tendency of N
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