written by Eric J. Ma on 2018-11-07 | tags: bayesian data science statistics

It’s definitely not easy work; anybody trying to tell you that you can "just apply this model and just be done with it" is probably wrong.

Let me clarify: I agree that doing the first half of the statement, "just apply this model", is a good starting point, but I disagree with the latter half, "and just be done with it". I have found that writing and fitting a very naive Bayesian model to the data I have is a very simple thing. But doing the right thing is not. Let’s not be confused: I don’t mean a Naive Bayes model, I mean naively writing down a Bayesian model that is structured very simply with the simplest of priors that you can think of.

Write down the model, including any transformations that you may need on the variables, and then lazily put in a bunch of priors. For example, you might just start with Gaussians everywhere a parameter could take on negative to positive infinity values, or a bounded Half Gaussian if it can only take values above (or below) a certain value. You might assume Gaussian-distributed noise in the output.

Let’s still not be confused: Obviously this would not apply to a beta-bernoulli/binomial model!

Doing the right thing, however, is where the tricky parts come in. To butcher and mash-up two quotes:

All models are wrong, but some are useful (Box), yet some models are more wrong than others (modifying from Orwell).

When doing modeling, a series of questions comes up:

- Do my naive assumptions about "Gaussians everywhere" hold?
- Given that my output data are continuous, is there a better distribution that can describe the likelihood?
- Is there are more principled prior for some of the variables?
- Does my link function, which joins the input data to the output parameters, properly describe their relationship?
- Instead of independent priors per group, would a group prior be justifiable?
- Does my model yield posterior distributions that are within bounds of reasonable ranges, which come from my prior knowledge? If it does not, do I need to bound my priors instead of naively assuming the full support for those distributions?

I am quite sure that this list is non-exhaustive, and probably only covers the bare minimum we have to think about.

Doing these model critiques is not easy. Yet, if we are to work towards truthful and actionable conclusions, it is a necessity. We want to know ground truth, so that we can act on it accordingly, and hence take appropriate actions.

I have experienced this modeling loop that Mike Betancourt describes (in his Principled Bayesian Workflow notebook) more than once. One involved count data, with a data scientist from TripAdvisor last year at the SciPy conference; another involved estimating cycle time distributions at work, and yet another involved a whole 4-parameter dose-response curve. In each scenario, model fitting and critique took hours at the minimum; I’d also note that with real world data, I didn’t necessarily get to the "win" was looking for.

With the count data, the TripAdvisor data scientist and I reached a point where after 5 rounds of tweaking his model, we had a model that fit the data, and described a data generating process that mimics closely to what we would expect given his process. It took us 5 rounds, and 3 hours of staring at his model and data, to get there!

Yet with cycle time distributions from work, a task ostensibly much easier ("just fit a distribution to the data"), none of my distribution choices, which reflected what I thought would be the data generating process, gave me a "good fit" to the data. I checked by many means: K-S tests, visual inspection, etc. I ended up abandoning the fitting procedure, and used empirical distributions instead.

With a 4-parameter dose-response curve, it took me 6 hours to go through 6 rounds of modeling to get to a point where I felt comfortable with the model. I started with a simplifying "Gaussians everywhere" assumption. Later, though, I hesitantly and tentatively putting in bound priors because I knew some posterior distributions were completely out of range under the naive assumptions of the first model, and were likely a result of insufficient range in the concentrations tested. Yet even that model remained unsatisfying: I was stuck with some compounds that didn’t change the output regardless of concentration, and that data are fundamentally very hard to fit with a dose response curve. Thus I the next afternoon,I modeled the dose response relationship using a Gaussian Process instead. Neither model is completely satisfying to the degree that the count data model was, but both the GP and the dose-response curve are and will be roughly correct modeling choices (with the GP probably being more flexible), and importantly, both are actionable by the experimentalists.

As you probably can see, whenever we either (1) don’t know ground truth, and/or (2) have
messy, real world data that don’t fit idealized assumptions about the data generating
process, **getting the model "right" is a very hard thing to do!** Moreover, data are
insufficient on their own to critique the model; we will always need to bring in prior
knowledge. Much as all probability is conditional probability (Venn), all modeling involves
prior knowledge. Sometimes it comes up in non-modellable ways, though as far as possible,
it’s a good exercise to try incorporating that into the model definition.

Even with that said, I’m still a fan of canned models, such as those provided by
`pymc-learn`

and `scikit-learn`

- provided we recognize that their "canned" nature and are
equipped to critique and modify said models. Yes, they provide easy, convenient baselines
that we can get started with. We can "just apply this model". But we can’t "just be done
with it": the hard part of getting the model right takes much longer and much more hard
work. *Veritas!*

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