The power of Normalizing Flows for Bayesian inference

          @misc{ scivideos_oai:cds.cern.ch:3025588,
            doi = {},
            url = {https://videos.cern.ch/record/3025588},
            author = {},
            keywords = {},
            language = {en},
            title = {The power of Normalizing Flows for Bayesian inference},
            publisher = {},
            year = {2026},
            month = {may},
            note = {oai:cds.cern.ch:3025588 see, \url{https://scivideos.org/cern-cds/3025588}}
          }
          

Abstract

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.

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