1 |
Use of finite mixture models with skew-t-normal Birnbaum- Saunders components in the analysis of wind speed: Case studies in Ontario, Canada Mahbudi S, Jamalizadeh A, Farnoosh R Renewable Energy, 162, 196, 2020 |
2 |
Gaussian process modelling with Gaussian mixture likelihood Daemi A, Kodamana H, Huang B Journal of Process Control, 81, 209, 2019 |
3 |
Process monitoring using a generalized probabilistic linear latent variable model Raveendran R, Kodamana H, Huang B Automatica, 96, 73, 2018 |
4 |
Robust probabilistic principal component analysis based process modeling: Dealing with simultaneous contamination of both input and output data Sadeghian A, Wu O, Huang B Journal of Process Control, 67, 94, 2018 |
5 |
Approaches to robust process identification: A review and tutorial of probabilistic methods Kodamana H, Huang B, Ranjan R, Zhao YJ, Tan RM, Sammaknejad N Journal of Process Control, 66, 68, 2018 |
6 |
Robust identification for nonlinear errors-in-variables systems using the EM algorithm Guo F, Hariprasad K, Huang B, Ding YS Journal of Process Control, 54, 129, 2017 |
7 |
Multi-model multivariate Gaussian process modelling with correlated noises Hong XD, Huang BA, Ding YS, Guo F, Chen L, Ren LH Journal of Process Control, 58, 11, 2017 |
8 |
Robust Gaussian process modeling using EM algorithm Ranjan R, Huang BA, Fatehi A Journal of Process Control, 42, 125, 2016 |
9 |
A robust forecasting framework based on the Kalman filtering approach with a twofold parameter tuning procedure: Application to solar and photovoltaic prediction Soubdhan T, Ndong J, Ould-Baba H, Do MT Solar Energy, 131, 246, 2016 |
10 |
Directly reconstructing principal components of heterogeneous particles from cryo-EM images Tagare HD, Kucukelbir A, Sigworth FJ, Wang HW, Rao M Journal of Structural Biology, 191(2), 245, 2015 |