no code implementations • 19 Jan 2022 • Camille Gontier, Simone Carlo Surace, Igor Delvendahl, Martin Müller, Jean-Pascal Pfister
Bayesian Active Learning (BAL) is an efficient framework for learning the parameters of a model, in which input stimuli are selected to maximize the mutual information between the observations and the unknown parameters.
1 code implementation • 21 Mar 2019 • Anna Kutschireiter, Simone Carlo Surace, Jean-Pascal Pfister
From there we continue our journey through discrete-time models, which is usually encountered in machine learning, and generalize to and further emphasize continuous-time filtering theory.
Methodology
no code implementations • 1 Nov 2016 • Simone Carlo Surace, Jean-Pascal Pfister
We revisit the problem of estimating the parameters of a partially observed diffusion process, consisting of a hidden state process and an observed process, with a continuous time parameter.