When everyone starts walking around the chilly streets of Toronto looking like they’re cosplaying the last 5 minutes of Call Me By Your Name, you know that Spring is in the air. Let’s celebrate the end of winter by pulling out our Liz Phair records, our slightly less-warm coats, and our hunger for long reads about some fundamentals.

I’ve always found the real sign that Spring has almost sprung is when strangers start asking which priors you should use on the variance parameters in a multilevel model.

A surprising twist on this age-old formula is that this year the question was actually asked on Twitter! Because I have a millennial’s^{-1} deep understanding of social media, my answer was “The best way to set priors on the variance of a random effect^{0} is to stop being a Nazi TERF”. But thankfully I have access to a blogging platform, so I’m going to give a more reasoned, nuanced, and calm answer here.

(**Warning: **This post is long. There’s a summary at the end. There is also an answer that fits into a 280 character limit. It obviously links to the Stan prior choice wiki. You can also look at this great Stan case study from Michael Betancourt.)

#### The book of love

In this post, I’m going to focus on weakly informative priors for the variance parameters in a multilevel model. The running example will be a very simple multilevel model for Poisson where the log-risk is modelled using a global intercept, a covariate, and one iid Gaussian random effect^{0}. Hence we only have one variance parameter in teh model that we need to find a prior for.

Some extensions beyond this simple model are discussed further down, but setting priors for those problems are much harder and I don’t have good answers for all of them. But hopefully there will be enough information here to start to see where we’re (we = Andrew, Aki, Michael and I, but also Håvard Rue, Sigrunn Sørbye, Andrea Riebler, Geir-Arne Fuglstad, and others) going with this thread of research.

#### Fallin’ out of love

The first thing I’m going to do is rule out any attempt to construct “non-informative” (whatever that means) priors. The main reason for this is that in most situations (and particularly for multilevel models), I don’t think vague priors are a good idea.

The main reason is that I don’t think they live up to their oft-stated ability to “let the data speak for itself”. The best case scenario is that you’re working with a very regular model and you have oodles of data. In this case, a correctly-specified vague prior (ie a reference prior) will not get in the way of the data’s dream to stand centre stage and sing out “*I am a maximum likelihood estimator to high order*“. But if that’s not the tune that your data should be singing (for example, if you think regularization or partial pooling may be useful), then a vague prior will not help.

Neither do I think that “letting the data speak” is the role of the prior distribution. *The right way to ensure that your model is letting the data speak for itself is through *a priori *and* a posteriori* model checking and model criticism. *In particular, well-constructed cross-validation methods, data splitting, posterior predictive checks, and prior sensitivity analysis are *always* necessary. It’s not enough to just say that you used a vague prior and hope. Hope has no place in statistics. * *

Most of the priors that people think are uninformative turn out not to be. The classic example is the $latex \text{Inverse-Gamma}(\epsilon,\epsilon)$ prior for the variance of a random effect^{0}. Andrew wrote a paper about this 12 years ago. It’s been read (or at least cited) quite a lot. The paper is old enough that children who weren’t even born when this paper was written have had time to rebel and take up smoking. And yet, the other day I read yet another paper that referred to a $latex \text{Inverse-Gamma}(0.001,0.001)$ prior on the variance of a random effect^{0} as “weakly informative”. So let me say it one more time for the people at the back: *It’s time to consign $latex \text{Inverse-Gamma}(\epsilon,\epsilon)$ priors to the dustbin of history.*

#### Love connection

But even people who did read Andrew’s paper^{0.25} can go wrong when they try to set vague priors. Here’s a “close to home” example using the default priors in INLA. INLA is an R package for doing fast, accurate, approximate Bayesian inference for latent Gaussian models, which is a small but useful class of statistical models that covers multilevel models, lots of spatial and spatiotemporal models, and some other things. The interface for the INLA package uses an extension to R’s formula environment (just like `rstanarm` and `brms`), and one of the things that this forces us to do is to specify default priors for the random effects^{0}. Here is a story of a default prior being bad.

This example is taken from the first practical that you can find here (should you want to reproduce it). The model, which will be our running example for the next little while, is a simple Poisson random effects^{0} model. It has 200 observations, and the random effect^{0} has 200 levels. For those of you familiar with the `lme4` syntax, the model can be fit as

` lme4::glmer(num_awards ~ math + (1|id),data=awards,family="poisson")`

If you run this command, you get a standard deviation of 0.57 for the random effect^{0}.

INLA parameterises a normal distribution using its precision (ie the inverse of the variance). This is in line with JAGS and BUGS, but different to Stan (which is the younger piece of software and uses the standard deviation instead). The default prior on the precision is $latex \text{Gamma}(1,5\cdot 10^{-5})$, which is essentially a very flat exponential prior.

The following table shows the 95% posterior credible intervals for the standard deviation across five different priors. The first is the INLA default; the second is recommended by Bernardinelli, Clayton, and Montomoli. Both of these priors are very vague. The third is a $latex \text{Inverse-Gamma}(\epsilon,\epsilon)$ prior with a not particularly small $latex \epsilon = 0.001$. The fourth and fifth are examples of PC priors which are designed to be weakly informative. For this model, the PC prior is an exponential prior on the standard deviation with the rate parameter tuned so that *a priori *$latex \Pr(\sigma >U) < 0.1$ with upper bounds $latex U=1$ and $latex U=10$ respectively.

2.5% | Median | 97.5% | |
---|---|---|---|

INLA default | 0.00 | 0.01 | 0.05 |

IG(0.5,0.0005) | 0.02 | 0.07 | 0.59 |

IG(0.001,0.001) | 0.04 | 0.40 | 0.75 |

PC(1,0.1) | 0.04 | 0.42 | 0.74 |

PC(10,0.1) | 0.09 | 0.49 | 0.79 |

The takeaway message here is that the two vague priors on the precision (the default priors in INLA and the one suggested by Bernardinelli *et al.*) are terrible for this problem. The other three priors do fine and agree with the frequentest estimate^{0.5}.

The take away from this should not be that INLA is a terrible piece of software with terrible priors. They actually work fairly well quite a lot of the time. I chose INLA as an example because it’s software that I know well. More than that, the only reason that I know anything at all about prior distributions is that we were trying to work out better default priors for INLA.

The point that we came to is that the task of finding one universal default prior for these sorts of parameters is basically impossible. It’s even more impossible to do it for more complex parameters (like lag-1 autocorrelation parameters, or shape parameters, or over-dispersion parameters). What is probably possible is to build a fairly broadly applicable default *system* for building default priors.

The rest of this post is a stab at explaining some of the things that you need to think about in order to build this default system for specifying priors. To do that, we need to think carefully about what we need a prior to do, as well as the various ways that we can make the task of setting priors a little easier.

#### To think that I once loved you

The first problem with everything I’ve talked about above is that, when you’re setting priors, it is a *terrible* idea to parameterize a normal distribution by its precision. No one has an intuitive feeling about the inverse of the variance of something, which makes it hard to sense-check a prior.

So why is it used? It simplifies all of the formulas when you’re dealing with hierarchical models. If

$latex y\mid x \sim N(x, \Sigma_y)$

$latex x \sim N(0, \Sigma_x)$,

then the variance of $latex x \mid y$ is $latex (\Sigma_x^{-1} + \Sigma_y^{-1})^{-1}$, whereas if we parameterize by the precision, we just need to add the precisions of $latex x$ and $latex y$ to get the precision of $latex x\mid y$. So when people sat down to write complicated code to perform inference on these models, they made their lives easier and parameterized normal distributions using the precision.

But just because something simplifies coding, it doesn’t mean that it’s a good idea to use it in general situations!

A *much better* way to parameterize the normal distribution is in terms of the *standard deviation. *The key benefit of the standard deviation is that it directly defines the *scale* of the random effect^{0}: we know that it’s highly likely that any realization of the random effect^{0} is within 3 standard deviations of the mean. This means that we can easily sense-check a prior on the standard deviation by asking “is this a sensible scale for the random effect^{0}?”.

For this model, the standard deviation is on the scale of the log-counts of a Poisson distribution, so we probably don’t want it to be too big. What does this mean? Well, the largest count observation that a computer is most likely only going to allow is 2,147,483,648. This is a fairly large number. If we want the *mean* of the Poisson to be most likely less than that (in the case where everything else in the log-risk is zero), then we want to ensure $latex \sigma < 7.2$. If we want to ensure that the *expected* count in each cell is less than a million, we need to ensure $latex \sigma < 4.6$. If we want to ensure the *expected *count to be less than 1000, we need to ensure $latex \sigma < 2.3$. (I can literally go on all day.) Under the Gamma(0.001,0.001) prior on the precision, the probabilities of these three events happening are, respectively, 0.01, 0.009, and 0.007. This means that rather than “speaking for itself” (whatever that means), your data is actually trying to claw back probability mass from some really dumb parts of the parameters space.

If you plot all of the distributions on the standard deviation scale (sample from them and do a histogram of their inverse square root), you see that the first 3 priors do not look particularly good. In particular, the first two are extremely concentrated around small values. This explains the extreme shrinkage of the standard deviation estimates. The third prior is extremely diffuse.

#### F.E.E.L.I.N.G.C.A.L.L.E.D.L.O.V.E.

So if the standard deviation is the correct parameter for setting a prior, what sort of prior should we set on it?

Well I have some good news for you: there are several popular alternatives and no clear winner. This is pretty common when you do research on priors. If you don’t screw up the prior completely, it’s a *higher-order effect*. This means that asymptotically, the effect of the prior on the posterior is smaller than the effect of sampling variation in the data.

But this didn’t happen in the above example, where there was a very clear effect. There are a few reasons for this. Firstly, the data only has 200 observations, so we may be so far from asymptopia the the prior is still quite important. The second reason is more interesting. There is more than one sensible asymptotic regime for this problem, and which one we should use depends on the details of the application.

The data, as it stands, has 200 records and the random effect^{0} has 200 different levels. There are essentially 3 different routes to asymptopia here. The first one is that we see an infinite number of repeated observations for these 200 levels. The second is a regime where we have $latex n$ observations, the random effect^{0} has $latex n$ levels, and we send $latex n\rightarrow \infty$. And the third option is half way between the two where the number of levels grows slower than the number of observations.

It is a possibly under-appreciated point that asymptotic assessments of statistical methods are conditional on the particular way you let things go to infinity. The “standard” asymptotic model of independent replication is quite often unrealistic and any priors constructed to take advantage of this asymptotic structure might have problems. This is particularly relevant to things like reference priors, where the asymptotic assumptions are strongly baked in to the prior specification. But it’s also relevant to any prior that has an asymptotic justification or any method for sense-checking priors that relies on asymptotic reasoning.

#### This girl’s in love with you

Well that got away from me, let’s try it again. What should a prior for the standard deviation of a Gaussian random effect^{0} look like?

The first thing is that it should peak at zero and go down as the standard deviation increases. Why? Because we need to ensure that our prior doesn’t prevent the model from easily finding the case where the random effect^{0} should not be in the model. The easiest way to ensure this is to have the prior decay away from zero. My experience is that this also prevents the model from over-fitting (in the sense of having too much posterior mass on very “exciting” configurations of the random effects^{0} that aren’t clearly present in the data). This idea that the prior should decrease monotonically away from a simple *base model* gives us some solid ground to start building our prior on.

The second thing that we want from a prior is *containment*. We want to build it so that it keeps our parameters in a sensible part of the model space. One way to justify this (and define “sensible”) is to think about the way Rubin characterized the posterior distribution in a very old essay (very old = older than me). He argued that you can conceptualize the posterior through the following procedure (here $latex y_\text{obs}$ is the observed data):

- Simulate $latex \theta_j \sim p(\theta)$
- Simulate $latex y_j \sim p(y \mid \theta)$
- If $latex y_j = y_\text{obs}$, then keep $latex \theta_j$, otherwise discard it.

The set of $latex \theta_j$ that are generated with this procedure are samples from the posterior distribution.

This strongly suggests that the prior should be built to ensure that the following containment property holds: The prior predictive distribution $latex p(y) = \int p(y \mid \theta)p(\theta)\,d\theta$ should be slightly broader than a prior predictive distribution that only has mass on plausible data sets.

This requirement is slightly different to Andrew’s original definition^{1} of a weakly informative prior (which is a prior on a parameter that is slightly broader than the best informative prior that we could put on a parameter without first seeing the data). The difference is that while the original definition of a WIP requires us to have some sort of interpretation of the parameter that we are putting the prior on, the containment condition only requires us to understand how a parameter affects the prior data generating process. Containment also reflects our view that The prior can often only be understood in the context of the likelihood.

If this all sounds like it would lead to very very complicated maths, it probably will. But we can always check containment visually. We have a discussion of this in our (re-written the other month to read less like it was written under intense deadline pressure) paper on visualization and the Bayesian workflow. That paper was recently accepted as a discussion paper in the Journal of the Royal Statistical Society Series A (date of the discussion meeting TBC) which is quite nice.

#### Standard Bitter Love Song #1

An immediate consequence of requiring this sort of containment condition is that we need to consider all of the parameters in our model simultaneously in order to check that it is satisfied. This means that we can’t just specify priors one at a time when a model has a number or random effects^{0}, or when there are other types of model component.

For the sake of sanity, we can simplify the process by breaking up the parameters into groups that affect different part of the data generating process. For instance, if you have several random effects^{0}, a good way to parameterize their variances is to put a prior on the standard deviation of the total variability and then a simplex-type prior to distribute the total standard deviation across the many components.

There are some complexities here (eg what do you do when the random effects^{0} have differing numbers of levels or differing dependence structures between levels?). Some suggested solutions are found towards the end of this paper and in this paper. A similar idea (except it’s the variances that are distributed rather than the standard deviations) is used for the default priors in rstanarm. The same idea pops up again for setting priors on mixture models.

#### I am leaving you because I don’t love you

So while that was a fun diversion, it still hasn’t given us a prior to use on the variance of a random effect^{0}. But we do now know what we’re looking for.

There are a number of priors that decay away from the base model and give us some form of containment. Here is an incomplete, but popular, list^{2} of simple containment priors:

- A half-Gaussian on the standard deviation
- An exponential on the standard deviation
- A half-t with 7 degrees of freedom on the standard deviation
- A half-t with 3 degrees of freedom on the standard deviation
- A half-Cauchy on the standard deviation.

These were listed from lightest to heaviest tail on purpose.

All of these priors require the specification of one parameter controlling the width of the 90% highest probability density interval^{2.5}. This parameter allows us to control containment. For example, if the random effect^{0} was part of a model for the log-relative risk of a disease, it may be sensible to set the parameter so that the probability that the standard deviation was less than 1 with 90% prior probability. This would correspond to the random effect^{0} being contained within [-3,3] and hence the random effect’s^{0} contribution to the relative risk be constrained to be a multiplicative factor contained within [0.05, 20].

This brings out an important aspect of containment priors: they are problem dependent. Although this example does not need a lot of expert information to set a sensible prior, it does need someone to understand how big a deviation from the baseline risk is unlikely for the particular scenario you are modelling. There isn’t a way around this. You either explicitly encode information about the problem in a prior, hide it in the structure of your asymptotic assumptions, or just throw your prior against a wall and hope that it sticks.

One way to standardize the procedure for setting priors is to demand that the model is correctly *scaled* in order to ensure that all of the parameters are on the unit scale. This can be done structurally (if your expected counts are well constructed, the relative risk will often be on unit scale). It can also be done by introducing some data-dependence into the prior, although this is a little more philosophically troublesome and you have to be diligent with your model assessment.

As for which of these priors is preferred, it really depends on context. If your model has a lot of random effects^{0}, or the likelihood is sensitive to extreme values of the random effect^{0}, you should opt for a lighter tail. On the other hand, a heavier tail goes some way towards softening the importance of correctly identifying the scale of the random effect^{0}.

To put some skin in the game, last time I talked to them about this^{3} Andrew seemed more in favour of good scaling and a half-Normal(0,1) prior, I like an exponential prior with an explicitly specified 90% quantile for the standard deviation, and Aki seemed to like one of the Student-t distributions. Honestly, I’m not sure it really makes a difference and with the speed of modern inference engines, you can often just try al three and see which works best. A nice^{4} simulation study in this direction was done by Nadja Klein and Thomas Kneib.

#### I love you but you’re dead

None of us are particularly fond of the half-Cauchy as a prior on the standard deviation. This was one of the early stabs at a weakly informative prior for the standard deviation. It does some nice things, for example if you marginalize out a standard deviation with a half-Cauchy prior, you get a distribution on the random effect^{0} that has a very heavy tail. This is the basis for the good theoretical properties of the Horseshoe prior for sparsity, as well as the good mean-squared error properties of the posterior mean estimator for the normal means problem.

But this prior has fallen out of favour with us for a couple of reasons. Firstly, the extremely heavy tail makes for a computational disaster if you’re actually trying to do anything with this prior. Secondly, it turns out that some regularization does good things for sparsity estimators.

But for me, it’s the third problem that sinks the prior. The tail is so heavy that if you simulate from the model you frequently get extremely implausible data sets. This means that the half-Cauchy prior on the standard deviation probably doesn’t satisfy our requirement that a good prior satisfies the containment property.

#### Can’t help lovin’ that man

Right. We’ve managed to select a good prior for the standard deviation for a Gaussian random effect^{0}. The next thing to think about is whether or not there are any generalizable lessons here. (Hint: There are.) So let us look at a very similar model that would be more computationally convenient to fit in Stan and see that, at least, all of the ideas above still work when we change the distribution of the random effect^{0}.

The role of the random effect^{0} in the example model is to account for over-dispersion in the count data (allowing the variance being larger than the mean). An alternative model that does the same thing is to take the likelihood as negative-binomial rather than Poisson. The resulting model no longer has a random effect^{0} (it’s a pure GLM). If you’re fitting this model in Stan, this is probably a good thing as the dimension of the parameter space goes from 203 to 3, which will definitely make your model run faster!

To parameterize the negative binomial distribution, we introduce an over-dispersion parameter $latex \phi$ with the property that the mean of the negative binomial is $latex \text{E}(y)=\mu$ and the variance is $latex \text{Var}(y)=\mu(1+\phi\mu)$.

We need to work out a sensible prior for the over-dispersion parameter. This is not a particularly well-explored topic in the Bayesian literature. And it’s not really obvious what a sensible prior will look like. The effect of $latex \phi$ on the distribution of $latex y$ is intertwined with the effect of $latex \mu$.

One way through this problem is to note that setting a prior on $latex \phi$ is in a lot of ways quite similar to setting a prior on the standard deviation of a Gaussian random effect^{0}.

To see this, we note that we can write the negative binomial with mean $latex \mu$ and overdispersion parameter $latex \phi$ as

$latex y \mid z \sim \text{Poisson}(z\mu)$

$latex z \sim \text{Gamma}(\phi^{-1},\phi^{-1})$.

This is different to the previous model. The Gamma distribution for $latex z$ has a heavier left tail and a lighter right tail than then log-normal distribution that was implied by the previous model. That being said, we can still apply all of our previous logic to this model.

The concept of the base model would be a spike at $latex z=1$, which is a Poisson distribution. (The argument for this is that it is a good base model because every other achievable model with this structure is more interesting as the mean and the variance are different from each other.) The base model occurs when $latex \phi = 0$.

So we now need to work out how to ensure containment for this type of model. The first thing to do is to try and make sure we have a sensible parameterization so that we can use one of our simple containment priors.

The gamma distribution has a mean of 1 and a variance of $latex \phi$, so one option would be to completely follow our previous logic and use $latex \sqrt{\phi}$ as a sensible transformation. Because the Gamma distribution is highly skewed, it isn’t completely clear that the standard deviation is as good a parameterization as it is in the previous model. But it turns out we can justify it a completely different way, which suggests it might be a decent choice.

A different method for finding a good parameterization for setting priors was explored in our PC priors paper. The idea is that we can parameterize our model according to its *deviation *from the base model. In both the paper and the rejoinder to the discussion, we give a pile of reasons why this is a fairly good idea.

In the context of this post, the thing that should be clear is that this method will not ensure containment directly. Instead, we are parameterizing the model by a deviation $latex d(\phi)$ so that if you increase the value of $latex d(\phi)$ by one unit, the model gets one unit more interesting (in the sense that the square root^{5} of the amount of information lost when you replace this model by the base model increases by one). The idea is that with this parameterization we will contain $latex z$ and hopefully therefore constrain $latex y$.

If we apply the PC prior re-parameterization to the Gaussian random effects^{0} model, we end up setting the prior on the standard deviation of the random effect^{0}, just as before. (This is a sense check!)

For a the Gamma random effect^{0}, some tedious maths leads to the exact form of the distance (Warning: this looks horrible. It will simplify soon.)

$latex d(\phi)=\sqrt{\lim_{\phi_0\rightarrow 0}\phi_0^{-1}KL\left[\text{Gamma}(\phi^{-1},\phi^{-1})\,||\,\text{Gamma}(\phi_0^{-1},\phi_0^{-1})\right]}=\sqrt{\log(\phi^{-1})-\psi(\phi^{-1})}$,

where $latex KL[\cdot \,||\, \cdot]$ is the Kullback-Leibler divergence, and $latex \psi(\cdot)$ is the digamma function (the derivative of the log-gamma function).

To simplify the distance, let’s take a look at what it looks like for very small and very large $latex \phi$. When $latex \phi$ is near zero, some rooting round the internet shows that $latex d(\phi)=\mathcal{O}(\sqrt{\phi})$. Similarly, when $latex \phi$ is large, we also get $latex d(\phi)=\mathcal{O}(\sqrt{\phi})$. Moreover, if you plot the square of the distance, it looks a lot like a straight line (it isn’t quite, but it’s very close). The suggests that putting a containment prior on the parameter $latex d(\phi)\approx\sqrt{\phi}$ might be a good idea.

So the end of this journey is that something like an appropriately scaled half-normal, exponential, or half-t distribution on $latex \sqrt{\phi}$ is a good candidate for a containment prior for the over-dispersion parameter of a negative binomial distribution. The truth of this statement can be evaluated using prior data simulations to check containment, and posterior sensitivity and predictive checks to check that the prior is appropriate for the problem at hand. Because no matter how good I think the mathematical argument is for a prior, it is still vital to actually verify the theory numerically for the particular problem you are trying to solve.

#### To bring you my love

If I had to summarize this very long post, I’d probably say the following:

- Vague priors tend to be “accidentally informative” in random effects
^{0}models (and other somewhat complicated statistical models) - The first thing you need to think about when setting a prior is to find a parameterization that is at least a little bit interpretable. If you can’t give an “in words” interpretation of your prior, you probably haven’t put enough care into setting it.
- Base models are very useful to work out how to set priors. Think about what the most boring thing that your model can do and expand it from there.
- The idea of containment is hiding in a lot of the ways people write about priors. It’s a good thing and we should pay more attention to it.
- Containment tells us explicitly that we need to consider the
*joint prior*on all parameters of the model, rather than just thinking about the priors on each parameter independently. Sometimes we can cheat if different sets of parameters change (almost) disjoint aspects of a model. - Containment also suggests that the priors need to respect the scale of the effect a parameter has. Sometimes we can fix this though a linear scaling (as in regression coefficients or standard deviation parameters), but sometimes we need to be more creative (as in the over-dispersion parameter).
- Containment makes it hard to specify a
*universal*default prior for a problem, but we can still specify a universal default*procedure*for setting priors for a problem. - We can
*always*check containment using prior simulations from the model. - We can
*always*assess the effect of the prior on the estimates through careful prior and posterior checks (this is really the story of our Visualization and Workflow paper). - These considerations stretch far beyond the problem of specifying priors for variance components in multilevel models.

##### Footnotes:

^{-1} For those of you who use “millennial” as a synonym for “young”, I promise you we are not. Young people were not alive when the Millennium turned. The oldest millennials are 35.

^{0} When I talk about random effects, I’m using the word in the sense of Hodges and Clayton, who have a lovely paper looks at how that term should be modernized. In particular, they point out the way that in modern contexts, random effects can be interpereted as “formal devices to facilitate smoothing or shrinkage, interpreting those terms broadly”. Everything that I talk about here holds for the scale parameter of a Gaussian process or a spatial random effect. Andrew and Aki prefer to just use different terms. The specific example that I’m using falls into their definition of an “old-style” random effect, in that the distribution of the random effect is of interest rather than the particular value of the realization isn’t of interest. I personally think that “random effect” is a sufficiently broad, accessible definition that bridges decades of theory with current practice, so I’m happy with it. But mileage obviously varies. (I have tagged the term every time it’s used in case someone gets half way through before they realize they’re not sure what I mean.)

^{0.25} Even if you use the suggested prior in Andrew’s paper (a half-Cauchy with scale parameter 25 on the standard deviation), bad things will happen. Consider a simple version of our problem, where $latex y\sim Po(e^x)$, $latex x\sim N(0,s^2)$. If we put the recommended half-Cauchy prior on $latex s$, then the simulation of $latex y$ will return an `NA` in R around 20% of the time. This happens because it tries to draw an integer bigger than $latex 2^{31}$, which is the largest integer that `R` will let you use. This will lead to very very bad inference and some fun numerical problems in small-data regimes.

^{0.5} Posterior intervals that are consistent with a particular estimator with some good frequentist properties isn’t necessarily a sign that a prior is good. Really there should be some proper posterior checks here. I have not done them because this is a blog.

^{1} Now I didn’t go back and check Andrew’s paper, so this may well be my memory of my original interpretation of Andrew’s definition of a Weakly Informative Prior. There are turtles everywhere!

^{2} These options are taken from the Stan prior choice wiki, which has a lot of interesting things on it.

^{2.5} I can’t say it controls the standard deviation because the half-Cauchy doesn’t have a well-defined standard deviation. So I picked a 90% highest density interval as the thing to be controlled. Note that because we’ve specified priors that decay away from the base model, these are one-sided intervals. Obviously if you want to use something else, use something else.

^{3} Obviously I do not speak for them here and you really shouldn’t take my half-memory of a conversation at some point in the dusty, distant past as a reflection of their actual views. But we do have a range of opinions on this matter, but not such a diverse set of opinions that we actually disagree with each other.

^{4} The exponential prior on the standard deviation (which is the PC prior for this model) did very well in these simulations, so obviously I very much like the results!

^{5} The square root is there for lots of good reasons, but mainly to get make sure all of the scales come out right. For the maths-y, it’s strongly suggested by Pinsker’s inequality.

I’ve been trying to name this writing style. How about “stream of ALL consciousness”?

https://en.m.wikipedia.org/wiki/Young_adult_(psychology)

Slight clarification. The decov prior in rstanarm sets the trace of a KxK covariance matrix to be equal to K times the square of a scale parameter, which in turn has a gamma prior but the defaults are unit shape and unit rate so it is really a unit-exponential. In the case that K = 1, this is just unit-exponential on the standard deviation of a normal distribution, although the whole thing gets scaled by the standard deviation of the errors in the outcome in Gaussian models. If K > 1, then the variances are equal to a simplex vector multiplied by the aforementioned trace and you can put a Dirichlet prior on that simplex. There is also a (Cholesky factor of a) correlation matrix that gets an LKJ prior.

Dan seems to prefer a scaled simplex for the vector of K standard deviations, which is almost the same thing. In any event, the decov prior seems to work well and we have had approximately zero questions on Discourse or the old Google Groups site where people were having trouble fitting a model with stan_[g]lmer and the answer was to fiddle with the hyperparameters of the decov prior.

Thanks for the clarification! I’m not surprised that it works (it’s pretty sensible!). My preference for distributing standard deviations directly is based on generalizability to situations where you don’t want the weights to be a priori exchangeable. This is much more interpretable on a standard deviation scale. But that’s really just choosing what to communicate. The prior comes from the same foundation, it’s just a different expression of the idea.

Now, if you were distributing the total precision across the simplex, I would probably feel differently. An example of a model that does this (or something similar) is the Leroux model in spatial statistics, which I am not fond of. (See equation 3 in this paper https://arxiv.org/pdf/1601.01180.pdf)

And the Lucky or Unlucky Girl IS?

Not a girl and largely imaginary. There was an earlier draft before I added more section titles where it looked a lot like I’d just had a bad break up and I’d taken a time machine to 1998. I added some happy songs to balance :p

I was kidding. Which songs?

This is a fantastic post (up to and including the bold choice to use non-integer and negative footnote numbering (but not including the subsection headings (not that my opinion on them matters))).

Thanks. What can I say, I love a theme and am not organized enough for my footnotes to be integers.

Personally, I care about the topic but got distracted by wondering, what’s the multiplicative inverse of a millennial?

A boomer?

Excellent post, and I have just a small comment to footnote 0:

Hodges and Clayton may redefine effects, but there is still the problem of “random”. What if the effects are deterministic but unknown? In general I prefer “unknown parameters” instead of “random parameters”.

I also came away asking “Why refer to new-style random effects as random effects?” instead of coming up with a new name for them, at least in a non-Bayesian setting. For posterior distributions, I think it is fine to refer to margins that are common to all groups and margins that are group-specific.

I think the idea is that there is a lot of similarity between new and old style random effects. So it’s an extension of an existing concept rather than a new one. Also these guys are biostatisticians, who are DRILLED on mixed effects models, so they’re building from a common ground.

Excellent post.

Thanks Keith

I think this deserves more thought:

> The first thing is that it should peak at zero and go down as the standard deviation

> increases. Why? Because we need to ensure that our prior doesn’t prevent the model from

> easily finding the case where the random effect^0 should not be in the model. The easiest way

> to ensure this is to have the prior decay away from zero.

That makes some sense in theory. But if there is any posterior mass in a small neighborhood of zero in the constrained space then there is mass out to negative infinity in the unconstrained space, and there will probably be divergent transition warnings from Stan in practice. So, it seems that you have to thread a needle where you are choosing a prior with a peak at zero in order to get a posterior whose mass is bounded away from zero but concentrated enough near zero that you discover that you are better off without that part of the model.

Yes, I did some limited experimentation with a half normal on the sd for ‘0 true variance random effects’ at some point and it wasn’t working as well as frequentist random effects. Then if I go to something ‘more frequentist’, like a normal(-1,10) on the log of the sd, sampling gets hairy and transitions get divergent.

This is a problem I’ve often encountered with a half-normal prior. The tails are too light so if the scale is even slightly wrong you are massively penalized for it.

The exponential seems to do much better, as does the t with 3-7 degrees of freedom (although lower dof has a higher chance of giving divergences)

You’re saying the light tail strongly affects inferences around the heavy region towards zero? This is not so intuitive…

What I meant to say was that a light rail meeans you sing escape the heavy region around zero without a lot of data, so that region (the bulk of the prior) needs to be wide enough to contain all the reasonable parameter values.

Yeah – this is a problem that you get when you transform bounded parameters. Honestly I don’t know how to deal with this intersection of prior specification and computation, but it’s definitely something needs thought.

Great post and very timely, because me and some colleagues were just having a discussion on priors for the over-dispersion parameter in negative binomial regression mdoels. In fact, another thing we contemplated is what the Jeffreys’s prior for such a model is. I know, I know, not a favored concept in this crowd, but I’d be interested if someone knows. One of the appeals to my mind is that the Jeffreys’s prior does behave reasonable well in a number of other situations. Additionally, I suspect it should permit an independent prior on the over-dispersion parameter, because I believe asymptotically the estimates of the rate and over-dispersion parameter are independent.

In any case… We are sort of in a funny situation, where all prior information strongly suggests that the dispersion parameter is >0. Usually the over-dispersion parameter is estimated to be >0 even in relatively large studies, which seems logical to me when we look at medical events happening to patients and we do not put much information on the patients in the model. I suspect our prior clearly should not prevent the model from finding the case where there are no random effects, but one of the worries is really that the prior should definitely not favor it (or values of the over-dispersion parameter near 0) “too much”. Whatever that means, but in a sense there would be an inappropriately precise / insufficiently uncertain estimate of any treatment effect (if we are talking about a randomized controlled clincial trial), if we concentrate too much posterior mass near the value zero for the over-dispersion parameter.

I wonder what kind of prior would work sensibly as a weakly informative prior in this sort of setting… Half-normal (or half-T) on the untransformed dispersion parameter (=quite flat towards zero)?!

I’m not sure if anyone has derived a Jeffreys’ prior for this model. I feel like it would be hard, but I’m genuinely not sure.

As for the case where you’re pretty sure there is over-dispersion, the base model at zero may still do well (in particular it’s useful for the case where your sample doesn’t show much overdispersion). Alternatively, you might want to put the base model somewhere else in the space and build a PC prior off that. An example where this has been done for the correlation parameter in a bivariate normal distribution is here (https://arxiv.org/pdf/1512.06217.pdf).

I think you’ll be fine in this case as long as your tail isn’t too light. Maybe a Student-t-7 would be a good idea. But the end point is you should try a couple of priors and see how they go on some existing data. You should also simulate some data that you think is realistic, but isn’t near the base model and see how the prior performs. If there’s anything that I wish I’d bought out more in the post, it’s the idea that we shouldn’t be looking for the one perfect prior, but rather a set of “good enough” priors that we can compare and check.

> shouldn’t be looking for the one perfect prior

That comment and the overall post has made me less uncertain that it is really all about getting a good enough (for exactly what?) probabilistic representation (model) for how the data came about and ended up being accessible to you (the analyst).

(e.g. “how you should best represent what you plan to act upon prior to acting in the world. The “how one should best represent” to profitably advance inquiry being logic” http://gelmanstatdev.wpengine.com/2017/09/27/value-set-act-represent-possibly-act-upon-aesthetics-ethics-logic/ )

Unfortunately, the bad meta-physics that supports the idea that there must/should be one perfect/true/best model leads many into an endless dizzying spiral towards the black hole of certainty – that can never be reached (as it presupposes direct access or correspondence to reality).

An example that comes to mind is Dennis Lindley’s quest for an axiom system for statistical inference as well as being overly reluctant to break from it https://www.youtube.com/watch?v=cgclGi8yEu4 (e.g. at around 18:45).