I just got back from attending this amazing conference in Paris:
http://www.go-isop.org/stan-for-pharmacometrics---paris-france
A few people were disturbed/surprised by the fact that I am linguist ("what are you doing at an pharmacometrics conference?"). I hasten to point out that two of the core developers of Stan are linguists too (Bob Carpenter and Mitzi Morris). People seem to think that all linguists do is correct other people's comma placements. However, despite my being a total outsider to the conference, the organizers were amazingly welcoming, and even allowed me to join in the speaker's dinner, and treated me like a regular guest.
Here is a quick summary of what I learnt:
1. Gelman's talk: The only thing I remember from his talk was the statement that when economists fit multiple regression models and find that one predictor's formerly significant effect was wiped out by adding another predictor, they think that the new predictor explains the old predictor. Which is pretty funny. Another funny thing was that he had absolutely no slides, and was drawing figures in the air, and apologizing for the low resolution of the figures.
2. Bob Carpenter gave an inspiring talk on the exciting stuff that's coming in Stan:
- Higher Speeds (Stan 2.10 will be 80 times faster with a 100 cores)
- Stan book
- New functionality (e.g., tuples, multivariate normal RNG)
- Gaussian process models will soon become tractable
- Blockless Stan is coming! This will make Stan code look more like JAGS (which is great). Stan will forever remain backward compatible so old code will not break.
My conclusion was that in the next few years, things will improve a lot in terms of speed and in terms of what one can do.
3. Torsten and Stan:
- Torsten seems to be a bunch of functions to do PK/PD modeling with Stan.
- Bill Gillespie on Torsten and Stan: https://www.metrumrg.com/wp-content/uploads/2018/05/BayesianPmetricsMBSW2018.pdf
- Free courses on Stan and PK/PK modeling: https://www.metrumrg.com/courses/
4. Mitzi Morris gave a great talk on disease mapping (accident mapping in NYC) using conditional autoregressive models (CAR). The idea is simple but great: one can model the correlations between neighboring boroughs. A straightforward application is in EEG, modeling data from all electrodes simultaneously, and modeling the decreasing correlation between neighbors. This is low-hanging fruit, esp. with Stan 2.18 coming.
5. From Bob I learnt that one should never provide free consultation (I am doing that these days), because people don't value your time then. If you charge them by the hour, this sharpens their focus. But I feel guilty charging people for my time, especially in medicine, where I provide free consulting: I'm a civil servant and already get paid by the state, and I get total freedom to do whatever I like. So it seems only fair that I serve the state in some useful way (other than studying processing differences in subject vs object relative clauses, that is).
For psycholinguists, there is a lot of stuff in pharmacometrics that will be important for EEG and visual world data: Gaussian process models, PK/PD modeling, spatial+temporal modeling of a signal like EEG. These tools exist today but we are not using them. And Stan makes a lot of this possible now or very soon now.
Summary: I'm impressed.
