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Software release strategies

Scheduled release strategy Stan’s moved to a scheduled release strategy where we’ll simply release whatever we have every three months. The Stan 2.20 release just went out last week. So you can expect Stan 2.21 in three months. Our core releases include the math library, the language compiler, and CmdStan. That requires us to keep […]

AnnoNLP conference on data coding for natural language processing

This workshop should be really interesting: Aggregating and analysing crowdsourced annotations for NLP EMNLP Workshop. November 3–4, 2019. Hong Kong. Silviu Paun and Dirk Hovy are co-organizing it. They’re very organized and know this area as well as anyone. I’m on the program committee, but won’t be able to attend. I really like the problem […]

Peter Ellis on Forecasting Antipodal Elections with Stan

I liked this intro to Peter Ellis from Rob J. Hyndman’s talk announcement: He [Peter Ellis] started forecasting elections in New Zealand as a way to learn how to use Stan, and the hobby has stuck with him since he moved back to Australia in late 2018. You may remember Peter from my previous post […]

Stan examples in Harezlak, Ruppert and Wand (2018) Semiparametric Regression with R

I saw earlier drafts of this when it was in preparation and they were great. Jarek Harezlak, David Ruppert and Matt P. Wand. 2018. Semiparametric Regression with R. UseR! Series. Springer. I particularly like the careful evaluation of variational approaches. I also very much like that it’s packed with visualizations and largely based on worked […]

StanCon 2019: 20–23 August, Cambridge, UK

It’s official. This year’s StanCon is in Cambridge. For details, see StanCon 2019 Home Page What can you expect? There will be two days of tutorials at all levels and two days of invited and submitted talks. The previous three StanCons (NYC 2017, Asilomar 2018, Helsinki 2018) were wonderful experiences for both their content and […]

Ben Lambert. 2018. A Student’s Guide to Bayesian Statistics.

Ben Goodrich, in a Stan forums survey of Stan video lectures, points us to the following book, which introduces Bayes, HMC, and Stan: Ben Lambert. 2018. A Student’s Guide to Bayesian Statistics. SAGE Publications. If Ben Goodrich is recommending it, it’s bound to be good. Amazon reviewers seem to really like it, too. You may […]

(Markov chain) Monte Carlo doesn’t “explore the posterior”

[Edit: (1) There’s nothing dependent on Markov chain—the argument applies to any Monte Carlo method in high dimensions. (2) No, (MC)MC is not not broken.] First some background, then the bad news, and finally the good news. Spoiler alert: The bad news is that exploring the posterior is intractable; the good news is that we […]

Book reading at Ann Arbor Meetup on Monday night: Probability and Statistics: a simulation-based introduction

The Talk I’m going to be previewing the book I’m in the process of writing at the Ann Arbor R meetup on Monday. Here are the details, including the working title: Probability and Statistics: a simulation-based introduction Bob Carpenter Monday, February 18, 2019 Ann Arbor SPARK, 330 East Liberty St, Ann Arbor I’ve been to […]

Google on Responsible AI Practices

Great and beautifully written advice for any data science setting: Google. Responsible AI Practices. Enjoy.

NYC Meetup Thursday: Under the hood: Stan’s library, language, and algorithms

I (Bob, not Andrew!) will be doing a meetup talk this coming Thursday in New York City. Here’s the link with registration and location and time details (summary: pizza unboxing at 6:30 pm in SoHo): Bayesian Data Analysis Meetup: Under the hood: Stan’s library, language, and algorithms After summarizing what Stan does, this talk will […]

Melanie Mitchell says, “As someone who has worked in A.I. for decades, I’ve witnessed the failure of similar predictions of imminent human-level A.I., and I’m certain these latest forecasts will fall short as well. “

Melanie Mitchell‘s piece, Artificial Intelligence Hits the Barrier of Meaning (NY Times behind limited paywall), is spot-on regarding the hype surrounding the current A.I. boom. It’s soon to come out in book length from FSG, so I suspect I’ll hear about it again in the New Yorker. Like Professor Mitchell, I started my Ph.D. at […]

A.I. parity with the West in 2020

Someone just sent me a link to an editorial by Ken Church, in the journal Natural Language Engineering (who knew that journal was still going? I’d have thought open access would’ve killed it). The abstract of Church’s column says of China, There is a bold government plan for AI with specific milestones for parity with […]

StanCon Helsinki streaming live now (and tomorrow)

We’re streaming live right now! Thursday 08:45-17:30: YouTube Link Friday 09:00-17:00: YouTube Link Timezone is Eastern European Summer Time (EEST) +0300 UTC Here’s a link to the full program [link fixed]. There have already been some great talks and they’ll all be posted with slides and runnable source code after the conference on the Stan […]

Three informal case studies: (1) Monte Carlo EM, (2) a new approach to C++ matrix autodiff with closures, (3) C++ serialization via parameter packs

Andrew suggested I cross-post these from the Stan forums to his blog, so here goes. Maximum marginal likelihood and posterior approximations with Monte Carlo expectation maximization: I unpack the goal of max marginal likelihood and approximate Bayes with MMAP and Laplace approximations. I then go through the basic EM algorithm (with a traditional analytic example […]

Thanks, NVIDIA

Andrew and I both received a note like this from NVIDIA: We have reviewed your NVIDIA GPU Grant Request and are happy support your work with the donation of (1) Titan Xp to support your research. Thanks! In case other people are interested, NVIDA’s GPU grant program provides ways for faculty or research scientists to […]

Advice on soft skills for academics

Julia Hirschberg sent this along to the natural language processing mailing list at Columbia: here are some slides from last spring’s CRA-W Grad Cohort and previous years that might be of interest. all sorts of topics such as interviewing, building confidence, finding a thesis topic, preparing your thesis proposal, publishing, entrepreneurialism, and a very interesting […]

Where do I learn about log_sum_exp, log1p, lccdf, and other numerical analysis tricks?

Richard McElreath inquires: I was helping a colleague recently fix his MATLAB code by using log_sum_exp and log1m tricks. The natural question he had was, “where do you learn this stuff?” I checked Numerical Recipes, but the statistical parts are actually pretty thin (at least in my 1994 edition). Do you know of any books/papers […]

Stan Workshop on Pharmacometrics—Paris, 24 July 2018

What: A one-day event organized by France Mentre (IAME, INSERM, Univ SPC, Univ Paris 7, Univ Paris 13) and Julie Bertrand (INSERM) and sponsored by the International Society of Pharmacometrics (ISoP). When: Tuesday 24 July 2018 Where: Faculté Bichat, 16 rue Henri Huchard, 75018 Paris Free Registration: Registration is being handled by ISoP; please click […]

The Manager’s Path (book recommendation for new managers)

I (Bob) was visiting Matt Hoffman (of NUTS fame) at Google in California a few weeks ago, and he recommended the following book: Camille Fournier. 2017. The Manager’s Path. O’Reilly. It’s ordered from being an employee, to being a tech lead, to managing a small team, to managing teams of teams, and I stopped there. […]

Mitzi’s talk on spatial models in Ann Arbor, Thursday 5 April 2018

Mitzi returns to her alma mater to give a talk at joint meeting of the Ann Arbor useR and ASA Meetups: Spatial models in Stan Abstract This case study shows how to efficiently encode and compute an intrinsic conditional autoregressive (ICAR) model in Stan. When data has a neighborhood structure, ICAR models provide spatial smoothing […]