DATA SCIENCE FOR ADVANCED LEARNERS:
January 19-21, 2022
*REGISTRATION CLOSED BUT WAITLIST IS OPEN*
In this workshop we will focus on Bayesian inference as well as optimization
and sampling with application to the Lotka-Volterra predator-prey system of
non-linear ordinary differential equations (ODEs). The first half of the
workshop will focus on the theoretical foundations for the problem of interest
and the second half will apply them using the Korali software from a sequential
and parallel perspective through the Python programming language.
Korali is a high-performance framework for Bayesian Uncertainty Quantification
(UQ), optimization, and reinforcement learning. Korali's multi-language
interface allows the execution of any type of computational model, either
sequential or distributed (MPI) using the C++ or Python programming languages.
Korali provides a simple interface that allows users to easily describe
statistical / deep learning problems and choose the algorithms to solve them.
Session 1 (THEORY):
What is Bayesian inference?
Optimization and sampling
Session 2 (THEORY/KORALI):
Bayesian inference for computational models
Inferring parameters for systems of ODEs
Application example: Lotka-Volterra (fit parameter with uncertainty)
Introduction to Korali and user interface via python
Session 3 (KORALI):
Practical examples in Korali
Running Korali in parallel