# The power of Normalizing Flows for Bayesian inference

- Institution: CERN
- Speaker: Eleonora Villa
- Year: 2026
- Language: English
- Source: [CERN](https://videos.cern.ch/record/3025588)
- Licence: [CERN](https://copyright.web.cern)
- Who can watch: Teens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

Pulsar Timing Array data analysis faces severe computational challenges as parameter spaces scale with the number of pulsars. I present two Normalizing Flows (NFs) based strategies to accelerate and improve Bayesian inference for stochastic gravitational wave background (SGWB). First, integrating NFs into the importance nested sampling framework i-nessai yields speedups of one to three orders of magnitude over standard methods, with robust posteriors and reliable evidence estimates. Second, a dual NFs architecture implementing parameter decorrelation via orthogonal projection disentangles pulsar noise from hyperprior parameters in hierarchical Bayesian modeling, enhancing noise constraining power even in the presence of a SGWB signal.

00:00:00 Slide 1

00:00:08 Slide 2

00:00:19 Slide 3

00:00:43 Slide 4

00:01:49 Slide 5

00:02:29 Slide 6

00:02:48 Slide 7

00:03:44 Slide 8

00:05:14 Slide 9

00:06:00 Slide 10

00:06:49 Slide 11

00:07:52 Slide 12

00:08:59 Slide 13

00:09:36 Slide 14

00:10:24 Slide 15

00:11:04 Slide 16

00:13:42 Slide 17

00:15:19 Slide 18

00:16:24 Slide 19

## Lectures

1. [The power of Normalizing Flows for Bayesian inference](https://invisiblenetwork.ai/learn/cern/3025588/b8a5f3cef735), 20 min (CERN's player)