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Automated method helps researchers quantify uncertainty in their predictions

A new technique can help researchers who use Bayesian inference achieve more accurate results more quickly, without a lot of additional work. ....

Tamara Broderick , Martin Ingram , Andrew Gelman , Bryan Giordano , Us Office Of Naval Research , Department Of Electrical Engineering , University Of California At Berkeley , National Science Foundation , Laboratory For Information , Columbia University , Institute For Data , Journal Of Machine Learning Research , Electrical Engineering , Computer Science , Decision Systems , Machine Learning , Bayesian Inference , Sample Average Approximation , Eterministic Advi ,

"Charging RF-Energy Harvesting Devices in IoT Networks with Imperfect C" by Hang Yu, Kwan Wu Chin et al.

This paper considers energy delivery by a Hybrid Access Point (HAP) to one or more Radio Frequency (RF)-energy harvesting devices. Unlike prior works, it considers imperfect and causal Channel State Information (CSI), and probabilistic constraints that ensure devices receive their required amount of energy over a given planning horizon. To this end, it outlines two novel contributions. The first is a chance-constrained program, which is then solved using a Mixed Integer Linear Program (MILP) coupled with a Sample Average Approximation (SAA) method. The second is a Model Predictive Control (MPC) solution that utilizes Gaussian Mixture Model (GMM) and a so called backoff that is used to tighten probabilistic constraints. The results show that the performance of the MPC based solution is within 8% of the optimal solution with a probability of 90.8%. ....

Integer Linear Program , Hybrid Access Point , Radio Frequency , Channel State Information , Mixed Integer Linear Program , Sample Average Approximation , Model Predictive Control , Gaussian Mixture Model , Internet Of Things , Power System Reliability , Robabilistic Logic , Eceding Horizon , Resource Management , Stochastic Optimization , Task Analysis , Wireless Charging ,