| 1 |
Learning Latent Variable Dynamic Graphical Models by Confidence Sets Selection
Ciccone V, Ferrante A, Zorzi M
IEEE Transactions on Automatic Control, 65(12), 5130, 2020 |
| 2 |
Process fault diagnosis via the integrated use of graphical lasso and Markov random fields learning & inference
Kim C, Lee H, Lee WB
Computers & Chemical Engineering, 125, 460, 2019 |
| 3 |
A novel approach to process operating mode diagnosis using conditional random fields in the presence of missing data
Fang MQ, Kodamana H, Huang BA, Sammaknejad N
Computers & Chemical Engineering, 111, 149, 2018 |
| 4 |
A Maximum Principle for Mean-Field SDEs with Time Change
Di Nunno G, Haferkorn H
Applied Mathematics and Optimization, 76(1), 137, 2017 |
| 5 |
Reversible Disorder in a Room Temperature Ferromagnet
Tomarken SL, Silevitch DM, Aeppli G, Brinkman BAW, Xu J, Dahmen KA, Rosenbaum TF
Advanced Functional Materials, 24(20), 2986, 2014 |
| 6 |
Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields
Xu YF, Choi J, Dass S, Maiti T
Automatica, 49(12), 3520, 2013 |
| 7 |
Maximum Likelihood Sequence Estimation for Hidden Reciprocal Processes
White LB, Vu HX
IEEE Transactions on Automatic Control, 58(10), 2670, 2013 |
| 8 |
Spatial prediction with mobile sensor networks using Gaussian processes with built-in Gaussian Markov random fields
Xu YF, Choi J
Automatica, 48(8), 1735, 2012 |
| 9 |
Modelling and Estimation for Finite State Reciprocal Processes
Carravetta F, White LB
IEEE Transactions on Automatic Control, 57(9), 2190, 2012 |
| 10 |
Decentralized coordination of autonomous swarms using parallel Gibbs sampling
Tan XB, Xi W, Baras JS
Automatica, 46(12), 2068, 2010 |
