Conor Goold writes:

I was reading this overview of mixed-effect modeling in ecology, and thought you or your blog readers may be interested in their last conclusion (page 35):

Other modelling approaches such as Bayesian inference are available, and allow much greater flexibility in choice of model structure, error structure and link function. However, the ability to compare among competing models is underdeveloped, and where these tools do exist, they are not yet accessible enough to non-experts to be useful.

This strikes me as quite odd. The paper discusses model selection using information criterion and model averaging in quite some detail, and it is confusing that the authors dismiss the Bayesian analogues (I presume they are aware of DIC, WAIC, LOO etc. [see chapter 7 of BDA3 and this paper — ed.]) as being ‘too hard’ when parts of their article would probably also be too hard for non-experts.

In an area in which small sample sizes are common, I’d argue that effort to explain Bayesian estimation in hierarchical models would have been very worthwhile (e.g. estimation of variance components, more accurate estimation of predictor coefficients using informative priors/variable selection).

In general, I find the ‘Bayesian reasoning is too difficult for non-experts’ argument pretty tiring, especially when it’s thrown in at the end of a paper like this!

Along these lines, I used to get people telling me that I couldn’t use Bayesian methods for applied problems because people wouldn’t stand for it. Au contraire, I’ve used Bayesian methods in many different applied fields for a long time, ever since my first work in political science in the 1980s, and nobody’s ever objected to it. If you don’t want to use some statistical method (Bayesian or otherwise) cos you don’t like it, fine; give your justification and go from there. But don’t ever say not to use a method out of a purported concern that some third party will object. That’s so bogus. Stand behind your own choices.

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