Welcome to the 11th event of the PyMCon Web Series!
Speaker: Abuzar Mahmood, Neuroscientist, Brandeis University
Event type: Live Talk with Q&A
Live Session Date/Time: 2023-11-20T14:00:00Z (subscribe here for email updates)
Website: PyMCon Events · PyMCon Web Series
Register for Live session: Meetup event (to get the Zoom link)
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).
The tutorial will be divided into three parts:
- Introduction to changepoint modelling and use cases.
- Construction of the “basic” multivariate changepoint model.
- Extensions to the “basic” model:
- Handling drift/noise in repeated observations using mixture emissions across multiple timeseries observations.
- Determining the distribution over number of changepoints using a Dirichlet Process prior.
Dr. Mahmood is a neuroscientist with a PhD from Brandeis University, where he investigated the neural coordination of taste. His research, initially using electrophysiology to probe brain region interactions, hints at a complex network processing flavors. His forthcoming studies aim to unravel this network further, exploring the directional flow of neural information and the impact of feedback mechanisms in taste perception.
Adia Lab is an independent, Abu Dhabi-based laboratory dedicated to basic and applied research in data and computational sciences.
ADIA Lab focuses on societally-important topics such as climate change and energy transition, blockchain technology, financial inclusion and investing, decision making, automation, cybersecurity, health sciences, education, telecommunications, and space, by conducting cutting-edge research in Data Science, Artificial Intelligence, Machine Learning, and High-Performance Computing.