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SUMMARY:Bayesian inference with likelihood reweighting: motivation\, metho
 d\, and application to gravitational-wave astrophysics - Isobel Romero-Sha
 w (DAMTP)
DTSTART:20221110T130000Z
DTEND:20221110T143000Z
UID:TALK177434@talks.cam.ac.uk
CONTACT:James Fergusson
DESCRIPTION:Bayesian inference is the workhorse of gravitational-wave astr
 ophysics. By analysing a gravitational-wave signal with computational Baye
 sian methods\, we obtain a posterior probability distribution the high-dim
 ensional parameter space that describes its source. This relies on computa
 tionally-intensive models for the signal\, which must be sufficiently effi
 cient that they can be evaluated hundreds of thousands of times per event.
  In the case that the model is not sufficiently efficient\, there is a sho
 rtcut: likelihood reweighting. In this talk\, I introduce Bayes theorem an
 d show how it is implemented for gravitational-wave astrophysics. I demons
 trate the logic behind likelihood reweighting\, and explore the different 
 situations in which it can be useful. I also give examples of the successf
 ul use of likelihood reweighting to measure the properties of gravitationa
 l-wave sources.
LOCATION:Hoyle Lecture Theatre\, Institute of Astronomy\, Madingley Rise
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