2 papers
stat.ME2024
Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods
Julia Walchessen, Amanda Lenzi, Mikael Kuusela
In spatial statistics, fast and accurate parameter estimation, coupled with a reliable means of uncertainty quantification, can be challenging when fitting a spatial process to rea…
stat.AP2024
Background Modeling for Double Higgs Boson Production: Density Ratios and Optimal Transport
Tudor Manole, Patrick Bryant, John Alison +2
We study the problem of data-driven background estimation, arising in the search of physics signals predicted by the Standard Model at the Large Hadron Collider. Our work is motiva…