standevbob

5 ms·

1.
by standevbob 6y ago
Stan supports optimization (L-BFGS) to find (penalized) maximum likelihood or MAP estimates where they exist. Bayesian estimates are typically posterior means, which involve MCMC rather than optimization, and the result is usually far away
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by standevbob 6y ago
That's right---Stan doesn't have any online learning facilities. It's very hard to approximate posteriors and chain them, so we don't try. If by "big data", we're talking about too big to fit in memory, t
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by standevbob 6y ago
This is the main reason that people use Stan---squeezing as much info out of your data as possible. That and the ability to write custom models for these situations. There are hundreds of different applications of Stan across the physical,
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by standevbob 6y ago
Stan requires models to be coded in the Stan language, which is a simple imperative language that's like MATLAB with explicit data types. This is the same as was done in Stan's predecessors, BUGS and JAGS. A Stan program can be r
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by standevbob 6y ago
Stan provides both frequentist inference (penalized maximum likelihood with bootstrapped confidence intervals) and Bayesian inference (MCMC sampling or approximate variational) inference. As currymj says, the differential equations (same fo
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by standevbob 6y ago
Yes, we regularly use Stan's MCMC to fit relatively simple time-series regression models or item-response theory type models with 10^5 parameters and 10^6 rows of data on a desktop computer. It can take a day, though. It's much