Machine Learning Accelerated Likelihood-Free Event Reconstruction in Dark Matter Direct Detection

no code implementations • 23 Oct 2018 • U. Simola, B. Pelssers, D. Barge, J. Conrad, J. Corander

Using the likelihood-free framework, a new algorithm to reconstruct the 2-D (x; y) position and the size of the charge signal (e) of an interaction is presented.

Bayesian Optimization BIG-bench Machine Learning +1

Orthogonal parallel MCMC methods for sampling and optimization

no code implementations • 30 Jul 2015 • L. Martino, V. Elvira, D. Luengo, J. Corander, F. Louzada

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning.

Bayesian Inference

Layered Adaptive Importance Sampling

no code implementations • 18 May 2015 • L. Martino, V. Elvira, D. Luengo, J. Corander

Monte Carlo methods represent the "de facto" standard for approximating complicated integrals involving multidimensional target distributions.