## penalising model component complexity

*“Prior selection is the fundamental issue in Bayesian statistics. Priors are the Bayesian’s greatest tool, but they are also the greatest point for criticism: the arbitrariness of prior selection procedures and the lack of realistic sensitivity analysis (…) are a serious argument against current Bayesian practice.” (p.23)*

**A** paper that I first read and annotated in the very early hours of the morning in Banff, when temperatures were down in the mid minus 20’s now appeared on arXiv, “Penalising model component complexity: A principled, practical approach to constructing priors” by Thiago Martins, Dan Simpson, Andrea Riebler, Håvard Rue, and Sigrunn Sørbye. It is a highly timely and pertinent paper on the selection of default priors! Which shows that the field of “objective” Bayes is still full of open problems and significant advances and makes a great argument for the future president [that I am] of the O’Bayes section of ISBA to encourage young Bayesian researchers to consider this branch of the field.

“On the other end of the hunt for the holy grail, “objective” priors are data-dependent and are not uniformly accepted among Bayesians on philosophical grounds.” (p.2)

**A**part from the above quote, as objective priors are *not* data-dependent! (this is presumably a typo, used instead of *model-dependent*), I like very much the introduction (appreciating the reference to the very recent Kamary (2014) that just got rejected by TAS for quoting my blog post way too much… and that we jointly resubmitted to Statistics and Computing). Maybe missing the alternative solution of going hierarchical as far as needed and ending up with default priors [at the top of the ladder]. And not discussing the difficulty in specifying the sensitivity of weakly informative priors.

“Most model components can be naturally regarded as a flexible version of a base model.” (p.3)

**T**he starting point for the modelling is the *base model*. How easy is it to define this base model? Does it [always?] translate into a null hypothesis formulation? Is there an automated derivation? I assume this somewhat follows from the “block” idea that I do like but how generic is model construction by blocks?

“Occam’s razor is the principle of parsimony, for which simpler model formulations should be preferred until there is enough support for a more complex model.” (p.4)

**I** also like this idea of putting a prior on the distance from the base! Even more because it is parameterisation invariant (at least at the hyperparameter level). (This vaguely reminded me of a paper we wrote with George a while ago replacing tests with distance evaluations.) And because it gives a definitive meaning to Occam’s razor. However, unless the hyperparameter ξ is one-dimensional this does not define a prior on ξ per se. I equally like Eqn (2) as it shows how the base constraint takes one away from Jeffrey’s prior. Plus, if one takes the Kullback as an intrinsic loss function, this also sounds related to Holmes’s and Walker’s substitute loss pseudopriors, no? Now, eqn (2) does not sound right in the general case. Unless one implicitly takes a uniform prior on the Kullback sphere of radius d? There is a feeling of one-d-ness in the description of the paper (at least till page 6) and I wanted to see how it extends to models with many (≥2) hyperparameters. Until I reached Section 6 where the authors state exactly that! There is also a potential difficulty in that d(ξ) cannot be computed in a general setting. (Assuming that d(ξ) has a non-vanishing Jacobian as on page 19 sounds rather unrealistic.) Still about Section 6, handling reference priors on correlation matrices is a major endeavour, which should produce a steady flow of followers..!

“The current practice of prior specification is, to be honest, not in a good shape. While there has been a strong growth of Bayesian analysis in science, the research field of “practical prior specification” has been left behind.” (*p.23)

**T**here are still quantities to specify and calibrate in the PC priors, which may actually be deemed a good thing by Bayesians (and some modellers). But overall I think this paper and its message constitute a terrific step for Bayesian statistics and I hope the paper can make it to a major journal.

April 2, 2014 at 4:36 pm

If I correctly understand the paper, in the case of Gaussian random effects model (pages 10 & 11), Martins et al propose to replace the usual Gamma(1,a) conjugate prior on the precision parameter by a Gamma(1,b) (exponential) prior on the standard deviation. In practice, this last parameterization on the standard deviation looks easier to implement than the type 2 Gumbel on the precision parameter into standard software eg Win(Open)bugs. It also allows some comparison with the uniform prior on SD proposed by A Gelman.

April 11, 2014 at 1:53 pm

You are correct ;-)