Dear Community,
my name is Caetano Souto Maior, and I would like to nominate myself for a one-year term in the Stan Governing Body.
I am currently a Postdoctoral Fellow in the Applied Statistics group at the Basque Center for Applied Mathematics (BCAM). My background, however, is mostly in the natural sciences, where I have been not only defending but also applying bayesian inference for almost a decade.
I am faculty at the FAES Graduate School @ NIH where I have taught Linear Algebra with Applications in Statistics, Single- and Multivariate Calculus, and recently developed the Mathematical Modeling for Life Scientists course. I am also a certified trainer at The Carpentries and a volunteer translator for Raspberry Pi.
I nominated myself last year, and although I wasn’t elected to one of four spots, I got an encouraging enough number of votes that I’d do it again this year. Then my proposals were centered around (i) a functional, education-oriented implementation of HMC methods, and (ii) dissemination of Stan in mechanistic modeling in the natural sciences. Although I don’t want to abandon those ideas completely, this time around I want to keep them in the back-burner as part of a broader perspective.
My intended contribution to the Stan Community can be summed up under two main topics:
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Better statistical literacy through Bayesian inference: Statistics is often seen by users as an impenetrable labyrinth of arcane rules and unclear use cases. For the initiated, Bayesian Statistics in particular is perceived by some to be a sort of sect requiring deep commitment to subjective philosophical principles or ideologies. Nothing could be farther form reality; if Statistics becomes “easy” with time it’s because experience teaches us that it all essentially boils down to parameter inference under some model(s), and Bayesian inference is especially well suited to paint that general picture. Stan and its community have played an important role in popularizing Bayesian practice and enabling state-of-the-art methods to be adopted widely. My proposal to this point is to improve/consolidate (existing efforts), and expand Stan’s role as a tool for teaching and training to improve statistical literacy broadly;
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End the “Bayesian wars” in scientific research: I got into an argument with a self-proclaimed non-frequentist who was critical of what they called “Bayesian propaganda” at a conference this summer. Despite some disagreement on my side I ended up accepting that almost everything related to inference can be done using either a frequentist or bayesian approach (as discussed recently here, MLE can be seen as a particular case of Bayesian inference, for instance). In turn, they conceded that Bayesian practices were usually more solid (possibly because they required a better understanding of the methods). Based on this and other discussions over the years I believe there is an opportunity to promote Bayesian inference (and by extension HMC and Stan as a state-of-the-art implementation) to scientists as simply Statistics and Inference, and do away with the nonsense surrounding “Bayesianism”. Concretely, my proposal here is to promote Bayesian approaches that “mimic” Frequentist methods (something brms, for instance has already done very successfully for linear models) rather than trying to (completely) replace them, avoiding conflict and making the introduction of the more solid practices smoother.
As I see it, these proposals are essentially orthogonal to those of this (and also probably last) year’s nominees, but as before it is also in line with my own and other’s efforts to promote Stan to different audiences in multiple research fields, different languages, in the Global South, and increase the diversity of the community, to mention some of the previous proposals by members of the SGB.
I am available to answer and questions or concerns, as well as to elaborate on the proposals summarized in the points above.
best,
Caetano