| 1 |
Efficient Learning for Selecting Important Nodes in Random Network
Li HD, Xu XY, Peng YJ, Chen CH
IEEE Transactions on Automatic Control, 66(3), 1321, 2021 |
| 2 |
Complementary relationship between small-hydropower and increasing penetration of solar photovoltaics: Evidence from CAISO
Shan R, Sasthav C, Wang XX, Lima LMM
Renewable Energy, 155, 1139, 2020 |
| 3 |
Evaluating regional climate-electricity demand nexus: A composite Bayesian predictive framework
Mukherjee S, Vineeth CR, Nateghi R
Applied Energy, 235, 1561, 2019 |
| 4 |
Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices
Brusaferri A, Matteucci M, Portolani P, Vitali A
Applied Energy, 250, 1158, 2019 |
| 5 |
Thompson Sampling for Stochastic Control: The Continuous Parameter Case
Banjevic D, Kim MJ
IEEE Transactions on Automatic Control, 64(10), 4137, 2019 |
| 6 |
Incipient sensor fault diagnosis in multimode processes using conditionally independent Bayesian learning based recursive transformed component statistical analysis
Shang J, Zhou DH, Chen MY, Ji HQ, Zhang HW
Journal of Process Control, 77, 7, 2019 |
| 7 |
A flexible state-space model for learning nonlinear dynamical systems
Svensson A, Schon TB
Automatica, 80, 189, 2017 |
| 8 |
Thompson Sampling for Stochastic Control: The Finite Parameter Case
Kim MJ
IEEE Transactions on Automatic Control, 62(12), 6415, 2017 |
| 9 |
A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems
Pan W, Yuan Y, Goncalves J, Stan GB
IEEE Transactions on Automatic Control, 61(1), 182, 2016 |
| 10 |
Online estimation of lithium-ion battery capacity using sparse Bayesian learning
Hu C, Jain G, Schmidt C, Strief C, Sullivan M
Journal of Power Sources, 289, 105, 2015 |
