Bani Mallick
Affiliations: | Texas A & M University, College Station, TX, United States |
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StatisticsGoogle:
"Bani Mallick"Children
Sign in to add traineeNicholas A. Rose | grad student | 2002 | Texas A & M |
Kyeong E. Lee | grad student | 2004 | Texas A & M |
Kyounghwa Bae | grad student | 2005 | Texas A & M |
Ilsung Chang | grad student | 2005 | Texas A & M |
Soma S. Dhavala | grad student | 2010 | Texas A & M |
Brian M. Hartman | grad student | 2010 | Texas A & M |
Bledar Konomi | grad student | 2011 | Texas A & M |
Anirban Mondal | grad student | 2011 | Texas A & M |
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Publications
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Guha N, Baladandayuthapani V, Mallick BK. (2020) Quantile Graphical Models: Bayesian Approaches. Journal of Machine Learning Research : Jmlr. 21: 1-47 |
Lee SY, Lei B, Mallick B. (2020) Estimation of COVID-19 spread curves integrating global data and borrowing information. Plos One. 15: e0236860 |
Ghosh RP, Mallick B, Pourahmadi M. (2020) Bayesian Estimation of Correlation Matrices of Longitudinal Data Bayesian Analysis |
Kundu S, Mallick BK, Baladandayuthapan V. (2019) Efficient Bayesian Regularization for Graphical Model Selection. Bayesian Analysis. 14: 449-476 |
Zhao K, Wulder MA, Hu T, et al. (2019) Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm Remote Sensing of Environment. 232: 111181 |
Song JJ, Mallick B. (2018) Hierarchical Bayesian models for predicting spatially correlated curves Statistics. 53: 196-209 |
Efendiev Y, Leung WT, Cheung SW, et al. (2017) BAYESIAN MULTISCALE FINITE ELEMENT METHODS. MODELING MISSING SUBGRID INFORMATION PROBABILISTICALLY International Journal For Multiscale Computational Engineering. 15: 175-197 |
Chakraborty A, Bingham D, Dhavala SS, et al. (2017) Emulation of Numerical Models With Over-Specified Basis Functions Technometrics. 59: 153-164 |
Zoh RS, Mallick B, Ivanov I, et al. (2016) PCAN: Probabilistic correlation analysis of two non-normal data sets. Biometrics |
Dreano D, Mallick B, Hoteit I. (2015) Filtering remotely sensed chlorophyll concentrations in the Red Sea using a space-time covariance model and a Kalman filter Spatial Statistics. 13: 1-20 |