Author: Bob Carpenter

Why does my academic lab keep growing?

Andrew, Breck, and I are struggling with the Stan group funding at Columbia just like most small groups in academia. The short story is that to apply for enough grants to give us a decent chance of making payroll in the following year, we have to apply for so many that our expected amount of […]

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 […]

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 […]

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

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 don’t need to. Sampling to characterize the posterior There’s a misconception among Markov chain Monte Carlo (MCMC) practitioners that the purpose of sampling is to […]

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

I (Bob, not Andrew!) will be doing a meetup talk next 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 focus […]

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Melanie Miller 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 Miller‘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 Miller, I started my Ph.D. at […]

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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 […]

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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 […]

The post Three informal case studies: (1) Monte Carlo EM, (2) a new approach to C++ matrix autodiff with closures, (3) C++ serialization via parameter packs appeared first on Statistical Modeling, Causal Inference, and Social Science.

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 […]

The post Three informal case studies: (1) Monte Carlo EM, (2) a new approach to C++ matrix autodiff with closures, (3) C++ serialization via parameter packs appeared first on Statistical Modeling, Causal Inference, and Social Science.

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 […]

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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 […]

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