Speakers
Felix Philipp Zinn
(Rheinisch Westfaelische Tech. Hoch. (DE))
Manfred Peter Fackeldey
(Princeton University (US))
Description
evermore is a software package for statistical inference using likelihood
functions of binned data. It fulfils three key concepts: performance,
differentiability, and object-oriented statistical model building.
evermore is build on JAX - a powerful autodifferentiation Python frame-
work. By making every component in evermore a “PyTree”, each compo-
nent can be jit-compiled (jax.jit), vectorized (jax.vmap) and differ-
entiated (jax.grad). This enables additionally novel computational
concepts, such as running thousands of fits simultaneously on a GPU
or differentiating through measurements of physical observables.
We present the key concepts of evermore, show its features, and discuss
performance benchmarks with toy datasets.
Authors
Felix Philipp Zinn
(Rheinisch Westfaelische Tech. Hoch. (DE))
Manfred Peter Fackeldey
(Princeton University (US))
