On the determination of probability density functions by using Neural Networks
Lluis Garrido, Aurelio Juste
Abstract
It is well known that the output of a Neural Network trained to disentangle between two classes has a probabilistic interpretation in terms of the a-posteriori Bayesian probability, provided that a unary representation is taken for the output patterns. This fact is used to make Neural Networks approximate probability density functions from examples in an unbinned way, giving a better performace than ``standard binned procedures''. In addition, the mapped p.d.f. has an analytical expression.
Create a lesson
Related papers
Transient Supercriticality in Hawkes Processes: Detection, Identifiability, and Apparent Criticality
D. Sornette
Near-exact Upper Confidence Limits for Axion Haloscopes
Andrea Gallo Rosso, Jan Conrad
Track finding and particle identification performance with particle physics foundation models
Shuhang Li, Haiwang Yu, Gabor Galgoczi et al.
Accelerating fits in high-energy physics and beyond
Amr Elmarassy, Sebastian Schmitt, Eluned Smith
Sorted Mutability as an Entropic Functional: Theory and Two Applications
Michel Aguilera, Eugenio E. Vogel, Francisco J. Peña et al.
Data geometry preserves prediction but reshapes explanation
Nicola Amoroso, Mario Caruso, Marianna La Rocca et al.