##Statistics Seminar##\\ Department of Mathematics and Statistics
^ **DATE:**|Thursday, October 5, 2023 |
^ **TIME:**|1:15pm -- 2:15pm |
^ **LOCATION:**|WH 100E |
^ **SPEAKER:**|Jingze Liu, Binghamton University |
^ **TITLE:**|High-dimensional Integration and Sampling with Normalizing Flows|
\\
**Abstract**
In many fields of science, high-dimensional integration is
required. Numerical methods have been developed to evaluate these
complex integrals. We introduce the code i-flow, a Python package that
performs high-dimensional numerical integration utilizing normalizing
flows. Normalizing flows are machine-learned, bijective mappings between
two distributions. i-flow can also be used to sample random points
according to complicated distributions in high dimensions. We compare
i-flow to other algorithms for high-dimensional numerical integration
and show that i-flow outperforms them for high dimensional correlated
integrals.