Treating uncertainties is essential in the design of safe aircraft, medical decision making, and many other fields. UM-Bridge enables straightforward uncertainty quantification (UQ) on advanced models by removing technical barriers.

Complex numerical models often consist of large code bases that are difficult to integrate with UQ packages such as PyMC, holding back many interesting applications. UM-Bridge is a universal interface for linking UQ and models, greatly accelerating development from prototype to high-performance computing.

This hands-on tutorial teaches participants how to build UQ applications using PyMC and UM-Bridge. We cover a range of practical exercises ranging from basic toy examples all the way to controlling parallelized models on a live cloud cluster. Beyond that, we encourage participants to bring their own methods and problems.

In many large surveys, not every respondent is asked every question, and not every respondent answers the questions they are asked. So how can we compare people who answer different sets of questions? One solution is to use item response theory (IRT) to impute missing responses – and nothing goes better with IRT than Bayesian methods!

In this talk, we report the results of a friendly competition – a bake-off – between two approaches to this problem, one using grid algorithms and a simplified model, the other using PyMC and a more detailed model. We’ll discuss the implementations, compare the results, and outline their pros and cons.

Dynamic data are all around us. Changepoint models allow us to know when changes happen in these data and what they look like. Probabilistic modelling allows us to elegantly build customizable changepoint models for different data types, as well as provide us with uncertainty estimates for the position and magnitude of the change (both indispensable quantities for decision-making and hypothesis testing). This tutorial will briefly cover building changepoint models for multivariate data using PyMC but will primarily focus on the ways in which this “basic” model can be extended.

This tutorial is targeted towards academic researchers, data scientists, and anyone interested in being able to easily build bespoke models which provide uncertainty estimates for inferred statistics. This talk will attempt to be accessible to beginners but leans towards more intermediate users interested in changepoint modelling. Previous experience with PyMC, and a background in statistical modelling is assumed. No libraries other than PyMC and the basic scientific stack (numpy, scipy, matplotlib) will be used.

The tutorial aims to be hands-on, will discuss some theory to provide context for the models discussed, and will be heavy on understanding code to construct the “guts” of the models (in particular, selection of distributions for modelling the emissions and changepoint locations, and the details of the tensor manipulation to put everything together).