I think what Andrew’s getting at is that these meetings aren’t really aimed at building software that most statisticians use. Most statisticians are using mainstream pre-written packages like glm() or lme4() or their equivalent in Stata or SAS, or something specialized for a given domain like NONMEM. We’re trying to aim at that kind of audience with packages like RStanArm and for an audience halfway in between that audience and Bayes Comp with something like Stan itself.

When we write grant proposals to work on Stan, we get dinged for not aiming to solve the kinds of problems tackled by papers at NIPS. We try to motivate that we’re fitting more elaborate models robustly (proposing things like internal online diagnostics and posterior predictive checks), but that doesn’t seem to go over too well with the crowd that wants brand new algorithms.

]]>I don’t think you were ranting. But even if you were, that’s fine: ranting is what blogging’s all about!

]]>Thanks for sharing your perspective. When blogging I offer only my own perspective, and it’s important that we have a comments section where others can offer their views. So it’s very helpful that you are commenting here, and I appreciate it.

In my above post, I wrote, “computational statistics often involves relatively easy problems.” I think this is indeed the case. Or, to put it another way, computational statistics often involves the hard problem of coming up with algorithms that automatically solve large numbers of easy problems. Computational physics often involves the problem of coming up with a single algorithm that, with great effort, solves a small class of hard problems.

But I’m in 100% agreement with you on your main point, which is that researchers work on all sorts of problems. Even if computational statistics often involves the development of automatic methods for relatively easy problems, that should not at all detract from the research being done in hard problems. Indeed, I can only be talking above about general tendencies, as every problem in computational statistical physics can anyway be thought of as a mathematical or statistical problem.

Just by analogy: Suppose someone said to me that 99.9% of applied statistics is the computation of p-values. I could well reply: Sure, but that’s not what I do! And if someone came to me and said that I should just stop doing what I’m doing and compute p-values instead, then I’d be annoyed too.

In my above post I was not trying do disparage anyone from research into the computational of normalizing constants; rather, I was talking about some general differences between different fields, and how the different problems that people work on can help us understand some differences in the literatures.

]]>https://warwick.ac.uk/fac/sci/statistics/crism/workshops/estimatingconstants/

hosted by a stats department, not a physics department, less than 2 years ago.

Since your blog is very popular, this type of confusion leads to other researchers in the field having to justify e.g. why we don’t simply use Stan instead of developing new algorithms. This happens to me regularly. Kind of annoying after a while.

]]>No, I have not left the field of computational statistics!

And no need to take offense: when I say “computational statistics often involves relatively easy problems,” there’s no disrespect intended. Relatively easy problems are important too. And it’s a hard problem to come up with algorithms that will work automatically for large classes of easy problems.

]]>http://www.worldscientific.com/worldscibooks/10.1142/P579

In it, you’ll find various interpretations of the normalizing constant in chemistry, molecular biology and physics (and most importantly, references). ]]>

I encourage readers interested in computational statistics (Monte Carlo, MCMC, SMC, computing Z, etc) to look up the list of presentations at conferences such as MCQMC or MCM’Ski (renamed Bayes Comp), both occurring every two years, or a good subset of JRSS B, JASA, NIPS and ICML papers (among others) to gather some ideas of not-so-easy problems in the field, and of which researchers are at the forefront of computational statistics.

]]>On another note, it is nice to see hierarchical modeling becoming more popular in astrostatistics. But, for the tough problems, things get really non-linear and GLM structure is not applicable. So non-centered approaches, prior scales, etc becoming really difficult. Hopefully, as the technique spreads through the field, we will see more tutorial-style examples of how to deal with these models.

]]>In statistical physics there are easy problems and hard problems. The easy problems get solved and we don’t think about them anymore; the hard problems get the attention. The Ising model is a famous hard problem: it’s a discrete model with phase transitions, requiring specialized algorithms to draw from the distribution; it’s a counterexample to the folk theorem of statistical computing.

In statistics, yes, there are some hard problems, but there are lots of easy problems that we have to keep solving over and over again. Linear regression, logistic regression, multilevel models, etc. Sampling from the posterior distributions of these models is not so hard.

In the world of physics you might have one model, one particular model, and spend years on it. In the world of statistics we have lots of little problems and we want to develop general tools to solve them without much user effort.

]]>Anyway, to the extent I understand any of this, unless there is some sort of immutable distribution of the distributions that account for variability among living things I suspect that physicists will always have it easier. The hydrogen atom is not (so far as I know) trying to exploit its environment by changing the distributions of its lonely electron’s various orbitals; yet an increasing number of papers are demonstrating the ability of living things to skew, etc. the phenotype of offspring to exploit detected changes in energy, predation, population density, mate availability, etc. in Mom’s environment.

]]>Isnt’ the reason the physicists seem to be worried about Z that they use models like Markov random fields where the normalizing constant Z depends on the parameters?

I haven’t seen the kinds of models physicists work on (largely through the lens of Michael) being much different than other statisticians. They’re just more physically grounded, which helps with formulating priors and interpreting results.

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