Wed 26 Aug 5pm Paris time (10am NY time):
The workflow of applied Bayesian statistics includes not just inference but also model building, model checking, confidence-building using fake data, troubleshooting problems with computation, model understanding, and model comparison. We move toward codifying these steps in the realistic scenario in which we are fitting many models for a given problem.
We’ve been talking about this for a long time!
Here’s a talk on the topic from 2011, and here’s a post from 2017 with some comments from others, and here’s an article from 2019 with Gabry et al. We even have some youtube videos on the topic. Let’s hope I don’t repeat too much the material from 2011, 2017, 2018, etc.
We’re in the midst of writing an article on the topic, trying to separate the computational workflow involved in successfully fitting a single model, from the statistical workflow involved in understanding a problem through a series of fitted models.
P.S. Zad sends in the above photo with caption, “When you order a black box algorithm from Amazon but forgot to read the reviews.”