Anirban Mondal
Cited by
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Bayesian uncertainty quantification for flows in heterogeneous porous media using reversible jump Markov chain Monte Carlo methods
A Mondal, Y Efendiev, B Mallick, A Datta-Gupta
Advances in Water Resources 33 (3), 241-256, 2010
SARS-CoV-2 infection in health care workers: A retrospective analysis and model study
Y Bai, X Wang, Q Huang, H Wang, D Gurarie, M Ndeffo-Mbah, F Fan, P Fu, ...
MedRxiv, 2020.03. 29.20047159, 2020
Analyzing stochastic computer models: A review with opportunities
E Baker, P Barbillon, A Fadikar, RB Gramacy, R Herbei, D Higdon, ...
Statistical Science 37 (1), 64-89, 2022
Risk factors of SARS-CoV-2 infection in healthcare workers: a retrospective study of a nosocomial outbreak
X Wang, X Jiang, Q Huang, H Wang, D Gurarie, M Ndeffo-Mbah, F Fan, ...
Sleep Medicine: X 2, 100028, 2020
Bayesian uncertainty quantification for subsurface inversion using a multiscale hierarchical model
A Mondal, B Mallick, Y Efendiev, A Datta-Gupta
Technometrics 56 (3), 381-392, 2014
SARS-CoV-2 transmission and control in a hospital setting: an individual-based modelling study
Q Huang, A Mondal, X Jiang, MA Horn, F Fan, P Fu, X Wang, H Zhao, ...
Royal Society Open Science 8 (3), 201895, 2021
Preconditioning Markov chain Monte Carlo method for geomechanical subsidence using multiscale method and machine learning technique
M Vasilyeva, A Tyrylgin, DL Brown, A Mondal
Journal of Computational and Applied Mathematics 392, 113420, 2021
Computer model emulation with high-dimensional functional output in large-scale observing system uncertainty experiments
P Ma, A Mondal, BA Konomi, J Hobbs, JJ Song, EL Kang
Technometrics 64 (1), 65-79, 2022
Stratified random sampling for dependent inputs in Monte Carlo simulations from computer experiments
A Mondal, A Mandal
Journal of Statistical Planning and Inference 205, 269-282, 2020
Computer model calibration based on image warping metrics: an application for sea ice deformation
Y Guan, C Sampson, JD Tucker, W Chang, A Mondal, M Haran, D Sulsky
Journal of Agricultural, Biological and Environmental Statistics 24, 444-463, 2019
A new approach for fast evaluations of large portfolios of oil and gas fields
D Castiñeira, A Mondal, S Matringe
SPE Annual Technical Conference and Exhibition?, SPE-170989-MS, 2014
Individual-based modeling of COVID-19 transmission in college communities
DG Durward Cator, Qimin Huang, Anirban Mondal, Martial Ndeffo-Mbah
Mathematical Biosciences and Engineering 19 (12), 13861-13877, 2022
History Matching Channelized Reservoirs Using Reversible Jump Markov Chain Monte Carlo Methods
J Xie, A Mondal, Y Efendiev, B Mallick, A Datta-Gupta
SPE Improved Oil Recovery Symposium, 2010
Bayesian Inference for COVID-19 Transmission Dynamics in India Using a Modified SEIR Model
K Yin, A Mondal, M Ndeffo-Mbah, P Banerjee, Q Huang, D Gurarie
Mathematics 10 (21), 4037, 2022
Spatial retrievals of atmospheric carbon dioxide from satellite observations
J Hobbs, M Katzfuss, D Zilber, J Brynjarsdóttir, A Mondal, V Berrocal
Remote Sensing 13 (4), 571, 2021
Efficient recovery of petroleum from reservoir and optimized well design and operation through well-based production and automated decline curve analysis
A Mondal, DC Areas, H Darabi, SF Matringe
US Patent 10,508,532, 2019
A Parametric Approach to Unmixing Remote Sensing Crop Growth Signatures
C Lewis-Beck, A Mondal, Z Zhu, JJ Song, J Hobbs
Journal of Agricultural, Biological and Environmental Statistics 24 (3), 502-516, 2019
A two-stage adaptive Metropolis algorithm
A Mondal, K Yin, A Mandal
Journal of Statistical Computation and Simulation, 2022
Bayesian uncertainty quantification for channelized reservoirs via reduced dimensional parameterization
A Mondal, J Wei
Mathematics 9 (9), 1067, 2021
Bayesian Uncertainty Quantification for Large Scale Spatial Inverse Problems
A Mondal
Texas A & M University, 2012
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