I've devoted much time to development of methods and software for validating predictive models in the traditional frequentist statistical domain. In putting more Bayesian ideas into practice and teaching I see some key differences to embrace. First, Bayesian predictive modeling asks the analyst to think hard about prior distributions that may be customized to the candidate features, and these priors will pull the model towards them (i.e., achieve shrinkage/penalization/regularization with different amounts of penalization for different predictive features). Second, the "real" Bayesian way does not result in a single model but one gets an entire posterior distribution for a prediction.

With those Bayesian features in mind, what does overfitting mean? Should we assess it? If so, how? How do we know when a Bayesian model is reliable for field use? Or is that a moot point since the posterior will carry along all of the caution-giving uncertainties when we use the model we developed for prediction?

How would the thinking change if we forced the Bayesian model to be distilled to a single number, e.g., posterior mean/mode/median risk?

I see some related thinking here. A parallel discussion may be found here.

Follow-up question: : If we are fully Bayesian and spend some time thinking about the priors before seeing the data, and we fit a model where the data likelihood was appropriately specified, are we compelled to be satisfied with our model with regard to overfitting? Or do we need to do what we do in the frequentist world where a randomly chosen subject may be predicted well on the average, but if we choose a subject who has a very low prediction or one having a very high predicted value there will be regression to the mean?


2 Answers 2


I might start by saying that a Bayesian model cannot systematically overfit (or underfit) data that are drawn from the prior predictive distribution, which is the basis for a procedure to validate that Bayesian software is working correctly before it is applied to data collected from the world.

But it can overfit a single dataset drawn from the prior predictive distribution or a single dataset collected from the world in the sense that the various predictive measures applied to the data that you conditioned on look better than those same predictive measures applied to future data that are generated by the same process. Chapter 6 of Richard McElreath's Bayesian book is devoted to overfitting.

The severity and frequency of overfitting can be lessened by good priors, particularly those that are informative about the scale of an effect. By putting vanishing prior probability on implausibly large values, you discourage the posterior distribution from getting overly excited by some idiosyncratic aspect of the data that you condition on that may suggest an implausibly large effect.

The best ways of detecting overfitting involve leave-one-out cross-validation, which can be approximated from a posterior distribution that does not actually leave any observations out of the conditioning set. There is an assumption that no individual "observation" [*] that you condition on has an overly large effect on the posterior distribution, but that assumption is checkable by evaluating the size of the estimate of the shape parameter in a Generalized Pareto distribution that is fit to the importance sampling weights (that are derived from the log-likelihood of an observation evaluated over every draw from the posterior distribution). If this assumption is satisfied, then you can obtain predictive measures for each observation that are as if that observation had been omitted, the posterior had been drawn from conditional on the remaining observations, and the posterior predictive distribution had been constructed for the omitted observation. If your predictions of left out observations suffer, then your model was overfitting to begin with. These ideas are implemented in the loo package for R, which includes citations such as here and there.

As far as distilling to a single number goes, I like to calculate the proportion of observations that fall within 50% predictive intervals. To the extent that this proportion is greater than one half, the model is overfitting, although you need more than a handful of observations in order to cut through the noise in the inclusion indicator function. For comparing different models (that may overfit), the expected log predictive density (which is calculated by the loo function in the loo package) is a good measure (proposed by I.J. Good) because it takes into account the possibility that a more flexible model may fit the available data better than a less flexible model but is expected to predict future data worse. But these ideas can be applied to the expectation of any predictive measure (that may be more intuitive to practitioners); see the E_loo function in the loo package.

[*] You do have to choose what constitutes an observation in a hierarchical model. For example, are you interested in predicting a new patient or a new time point for an existing patient? You can do it either way, but the former requires that you (re)write the likelihood function to integrate out the patient-specific parameters.

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    $\begingroup$ Very informative Ben. Thanks very much for taking the time to respond in detail. To answer your question about the scope, I'm referring to new patients. I'm left with a the general philosophical question which I've added to the end of the original question. $\endgroup$ Commented May 3, 2018 at 15:45
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    $\begingroup$ I tend to think of checks like these as reflecting aspects of our prior beliefs that we did not or could not build into the prior distributions we used. For example, in principle, you should be specifying a joint prior PDF over all the parameters, but almost always there is a lot of assuming that this is independent of that, a priori, not because you really believe they are independent but just because specifying the multivariate dependence structure is very difficult. Multivariate functions such as predictions can help tell you after the fact, whether the inputs were jointly sensible. $\endgroup$ Commented May 3, 2018 at 18:52
  • $\begingroup$ That makes tremendous sense and is very insightful. I'm still left with a bit of a quandary about the assessment of predictive accuracy for "extreme" subjects, i.e., those with very low or very high predicted values. [And for Bayes, which predicted values. Is it those subjects with a shifted posterior distribution or those with a low/high posterior mean?] $\endgroup$ Commented May 4, 2018 at 10:59
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    $\begingroup$ Another thought on this: It seems in many situations, practitioners have fairly coherent and non-controversial beliefs about the denominator of Bayes Rule. For example, if someone has this or that cancer, what is their distribution of survival time without conditioning on anything else? But it is harder and more controversial to specify the numerator of Bayes Rule such that if you integrate out all the parameters, you are left with what you believe the denominator to be. Predictive checking (both prior and posterior) is kind of a way to align the numerator with the denominator of Bayes Rule. $\endgroup$ Commented May 6, 2018 at 17:46

Overfitting means the model works well on the training set but performs poorly on test set. IMHO, it comes from two sources: the data and the model we use (or our subjectivity).

Data is probably the more important factor. With whatever models/approaches we use, we implicitly assume the our data is representative enough, that is what we obtain from our (training) data can be also generalized to the population. In practice it is always not the case. If the data is not iid then standard $k$-fold CV makes no sense in avoiding overfitting.

As a result, if we are frequentist then the source of overfitting comes from MLE. If we are Bayesian then this comes from the (subjective) choice of prior distribution(and of course the choice of likelihood)). So even if you use posterior distribution/mean/median, you already overfitted from the beginning and this overfitting is carried along. The proper choice of prior distribution and likelihood will help but they are still the models, you can never avoid overfitting completely.

  • $\begingroup$ Ignoring the data likelihood, which is in common for frequentist and Bayesian approaches, the idea that overfitting comes from the choice of the prior is insightful. That implies that there is no way to check for overfitting, because there is no way nor need to check the prior if we've done all our pre-data thinking about the prior in advance. But still I'm left with the sinking feeling that extreme predictions will show overfitting (regression to the mean). The prior is about parameters, not about extremes in data. $\endgroup$ Commented May 6, 2018 at 11:48

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