Abstract: Gaussian process state-space models (GPSSMs) offer a principled framework for learning and inference in nonlinear dynamical systems with uncertainty quantification. However, existing GPSSMs ...

This paper proposes a novel approach combining prior physics-based Gaussian Process Regression (GPR) with Bayesian Optimization for efficient and accurate electromagnetic near-field scanning. By ...

Matt Webber is an experienced personal finance writer, researcher, and editor. He has published widely on personal finance, marketing, and the impact of technology on contemporary arts and culture.

Millions of voters will head to the polls on Election Day to choose the next president. But once a voter casts a ballot, what happens next and how does the counting process work? By David Taintor, Sam ...

This study provides important insights into how working memory shapes perceptual decisions, using a dual-task design, continuous mouse tracking, and hierarchical Bayesian modeling. By dissociating ...

This work introduces a new scalable model-free actor-critic based algorithm based on Proximal Policy Optimization that uses a deep Gaussian process to directly approximate both the policy and the ...