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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 […]

Bob’s talk at Berkeley, Thursday 22 March, 3 pm

It’s at the Institute for Data Science at Berkeley. Hierarchical Modeling in Stan for Pooling, Prediction, and Multiple Comparisons 22 March 2018, 3pm 190 Doe Library. UC Berkeley. And here’s the abstract: I’ll provide an end-to-end example of using R and Stan to carry out full Bayesian inference for a simple set of repeated binary […]

Andrew vs. the Multi-Armed Bandit

Andrew and I were talking about coding up some sequential designs for A/B testing in Stan the other week. I volunteered to do the legwork and implement some examples. The literature is very accessible these days—it can be found under the subject heading “multi-armed bandits.” There’s even a Wikipedia page on multi-armed bandits that lays […]

When to add a feature to Stan? The recurring issue of the compound declare-distribute statement

At today’s Stan meeting (this is Bob, so I really do mean today), we revisited the topic of whether to add a feature to Stan that would let you put distributions on parameters with their declarations. Compound declare-define statements Mitzi added declare-define statements a while back, so you can now write: transformed parameter { real […]

New Stan case studies: NNGP and Lotka-Volterra

It’s only January and we already have two new case studies up on the Stan site. Two new case studies Lu Zhang of UCLA contributed a case study on nearest neighbor Gaussian processes. Bob Carpenter (that’s me!) of Columbia Uni contributed one on Lotka-Volterra population dynamics. Mitzi Morris of Columbia Uni has been updating her […]

We were measuring the speed of Stan incorrectly—it’s faster than we thought in some cases due to antithetical sampling

Aki points out that in cases of antithetical sampling, our effective sample size calculations were unduly truncated above at the number of iterations. It turns out the effective sample size can be greater than the number of iterations if the draws are anticorrelated. And all we really care about for speed is effective sample size […]

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 […]

Interactive visualizations of sampling and GP regression

You really don’t want to miss Chi Feng‘s absolutely wonderful interactive demos. (1) Markov chain Monte Carlo sampling I believe this is exactly what Andrew was asking for a few Stan meetings ago: Chi Feng’s Interactive MCMC Sampling Visualizer This tool lets you explore a range of sampling algorithms including random-walk Metropolis, Hamiltonian Monte Carlo, […]

How not to compare the speed of Stan to something else

Someone’s wrong on the internet And I have to do something about it. Following on from Dan’s post on Barry Gibb statistical model evaluation, here’s an example inspired by a paper I found on Google Scholar searching for Stan citations. The paper (which there is no point in citing) concluded that JAGS was faster than […]

Computational and statistical issues with uniform interval priors

There are two anti-patterns* for prior specification in Stan programs that can be sourced directly to idioms developed for BUGS. One is the diffuse gamma priors that Andrew’s already written about at length. The second is interval-based priors. Which brings us to today’s post. Interval priors An interval prior is something like this in Stan […]