Ryan Giordano wrote: Last year at StanCon we talked about how you can differentiate under the integral to automatically calculate quantitative hyperparameter robustness for Bayesian posteriors. Since then, I’ve packaged the idea up into an R library that plays nice with Stan. You can install it from this github repo. I’m sure you’ll be pretty busy at StanCon, but I’ll be there presenting a poster about exactly this work, and if you have a moment to chat I’d be very interested to hear what you think! I’ve started applying this package to some of the Stan examples, and it’s already uncovered some (in my opinion) serious problems, like this one from chapter 13.5 of the ARM book. It’s easy to accidentally make a non-robust model, and I think a tool like this could be very useful to Stan users! As…
Original Post: Static sensitivity analysis: Computing robustness of Bayesian inferences to the choice of hyperparameters
StanCon 2018 Live Stream — bad news…. not enough bandwidth Posted by Daniel on 10 January 2018, 4:43 am Breaking news: no live stream. We’re recording, so we’ll put the videos online after the fact. We don’t have enough bandwidth to live stream today. StanCon 2018 starts today! We’re going to try our best to live stream the event on YouTube. We have the same video setup as last year, but may be limited by internet bandwidth here at Asilomar. If we’re up, we will these YouTube events on the Stan YouTube Channel (all times Pacific):
Original Post: StanCon 2018 Live Stream — bad news…. not enough bandwidth
Three new domain-specific (embedded) languages with a Stan backend One is an accident. Two is a coincidence. Three is a pattern. Perhaps it’s no coincidence that there are three new interfaces that use Stan’s C++ implementation of adaptive Hamiltonian Monte Carlo (currently an updated version of the no-U-turn sampler). ScalaStan embeds a Stan-like language in Scala. It’s a Scala package largely (if not entirely written by Joe Wingbermuehle.[GitHub link] tmbstan lets you fit TMB models with Stan. It’s an R package listing Kasper Kristensen as author.[CRAN link] SlicStan is a “blockless” and self-optimizing version of Stan. It’s a standalone language coded in F# written by Maria Gorinova.[pdf language spec] These are in contrast with systems that entirely reimplement a version of the no-U-turn sampler, such as PyMC3, ADMB, and NONMEM.
Original Post: Three new domain-specific (embedded) languages with a Stan backend
It looks pretty cool! Wednesday, Jan 10 Invited Talk: Predictive information criteria in hierarchical Bayesian models for clustered data. Sophia Rabe-Hesketh and Daniel Furr (U California, Berkely) 10:40-11:30am Does the New York City Police Department rely on quotas? Jonathan Auerbach (Columbia U) 11:30-11:50am Bayesian estimation of mechanical elastic constants. Ben Bales, Brent Goodlet, Tresa Pollock, Linda Petzold (UC Santa Barbara) 11:50am-12:10pm Joint longitudinal and time-to-event models via Stan. Sam Brilleman, Michael Crowther, Margarita Moreno-Betancur, Jacqueline Buros Novik, Rory Wolfe (Monash U, Columbia U) 12:10-12:30pmLunch 12:30-2:00pm ScalaStan. Joe Wingbermuehle (Cibo Technologies) 2:00-2:20pmA tutorial on Hidden Markov Models using Stan. Luis Damiano, Brian Peterson, Michael Weylandt 2:20-2:40pm Student Ornstein-Uhlenbeck models served three ways (with applications for population dynamics data). Aaron Goodman (Stanford U) 2:40-3:00pm SlicStan: a blockless Stan-like language. Maria I. Gorinova, Andrew D. Gordon, Charles Sutton (U of Edinburgh) 3:00-3:20pmBreak 3:20-4:00pm…
Original Post: StanCon is next week, Jan 10-12, 2018
Donald Williams points us to this new paper by Gang Chen, Yaqiong Xiao, Paul Taylor, Tracy Riggins, Fengji Geng, Elizabeth Redcay, and Robert Cox: In neuroimaging, the multiplicity issue may sneak into data analysis through several channels . . . One widely recognized aspect of multiplicity, multiple testing, occurs when the investigator fits a separate model for each voxel in the brain. However, multiplicity also occurs when the investigator conducts multiple comparisons within a model, tests two tails of a t-test separately when prior information is unavailable about the directionality, and branches in the analytic pipelines. . . . More fundamentally, the adoption of dichotomous decisions through sharp thresholding under NHST may not be appropriate when the null hypothesis itself is not pragmatically relevant because the effect of interest takes a continuum instead of discrete values and is not expected…
Original Post: “Handling Multiplicity in Neuroimaging through Bayesian Lenses with Hierarchical Modeling”
Bob Carpenter writes: Here’s what we do and what we recommend everyone else do: 1. code the model as straightforwardly as possible 2. generate fake data 3. make sure the program properly codes the model 4. run the program on real data 5. If the model is too slow, optimize one step at a time and for each step, go back to (3). The optimizations can be of either the statistical or the computational variety. Slow iterations can be due to computational statistical problems with parameterization (requiring too many iterations) or due to slow code (each log density and derivative evaluation being too slow).
Original Post: Workflow, baby, workflow
StanCon2018: one month to go, schedule finalized, over 20 talks, 6 tutorials… and flights are cheap Posted by Daniel on 9 December 2017, 9:17 pm StanCon2018 is shaping up nicely as a unique opportunity to immerse oneself in all things Stan, meet Stan developers and fellow users. Registration is still open, but spots are filling up fast. We’re at 130 registrants and counting! The draft schedule is now up. We have 16 accepted talks and 6 invited talks. Posters are still being accepted for our “Wear your poster” reception. There are 6 tutorials: Intro to Stan (8hrs over 4 sessions) Advanced Hierarchical Models Gaussian Processes How to develop for Stan at the C++ level Bayesian Decision Making for Executives and Those who Communicate with them Have I Converged Successfully? How to verify fit and diagnose fit problems. And there’s a 2…
Original Post: StanCon2018: one month to go, schedule finalized, over 20 talks, 6 tutorials… and flights are cheap
Using Stan to improve rice yields Posted by Andrew on 7 November 2017, 9:03 am Matt Espe writes: Here is a new paper citing Stan and the rstanarm package. Yield gap analysis of US rice production systems shows opportunities for improvement. Matthew B. Espe, Kenneth G. Cassman, Haishun Yang, Nicolas Guilpart, Patricio Grassini, Justin Van Wart, Merle Anders, Donn Beighley, Dustin Harrell, Steve Linscombe, Kent McKenzie, Randall Mutters, Lloyd T. Wilson, Bruce A. Linquist. Field Crops Research. Volume 196, September 2016, Pages 276–283. Many thanks to everyone on the development team for some excellent tools! I’ve not read the paper, but, hey, if Stan can improve U.S. rice yields by a factor of 1.5, that’s cool. Then all our research will have been worth it.
Original Post: Using Stan to improve rice yields
StanCon is happening at the beautiful Asilomar conference facility at the beach in Monterey California for three days starting January 10, 2018. We have space for 200 souls and this will sell out. If you don’t already know, Stan is the rising star of probabilistic modeling with Bayesian analysis. If you do statistics, machine learning or data science then you need to know about Stan. StanCon offers a full schedule of invited talks, submitted papers, and tutorials unavailable in any other format. Balancing the intellectual intensity of cutting edge statistical modeling are fun activities like indoor R/C airplane building/flying/designing and non-snobby blind wine tasting for after dinner activities. We will have the first ever “wear your poster” reception–see the call for posters below. And no parallel sessions–you get the entire StanCon2018, not a slice. Go to http://mc-stan.org/events/stancon2018 and register. Invited…
Original Post: StanCon2018 Early Registration ends Nov 10
Stan Weekly Roundup, 28 July 2017 Here’s the roundup for this past week. Michael Betancourt added case studies for methodology in both Python and R, based on the work he did getting the ML meetup together: Michael Betancourt, along with Mitzi Morris, Sean Talts, and Jonah Gabry taught the women in ML workshop at Viacom in NYC and there were 60 attendees working their way up from simple linear regression, through Poisson regression to GPs. Ben Goodrich has been working on new R^2 analyses and priors, as well as the usual maintenance on RStan and RStanArm. Aki Vehtari was at the summer school in Valencia teaching Stan. Aki has also been kicking off planning for StanCon in Helsinki 2019. Can’t believe we’re planning that far ahead! Sebastian Weber was in Helsinki giving a talk on Stan, but there weren’t many…
Original Post: Stan Weekly Roundup, 28 July 2017