| Bayesian and variational Bayesian approaches for flows in heterogeneous random media K Yang, N Guha, Y Efendiev, BK Mallick Journal of Computational Physics 345, 275-293, 2017 | 19 | 2017 |
| A variational Bayesian approach for inverse problems with skew-t error distributions N Guha, X Wu, Y Efendiev, B Jin, BK Mallick Journal of Computational Physics 301, 377-393, 2015 | 18 | 2015 |
| Nonparametric Bayesian methods for benchmark dose estimation N Guha, A Roy, L Kopylev, J Fox, M Spassova, P White Risk Analysis 33 (9), 1608-1619, 2013 | 17 | 2013 |
| Multilevel approximate Bayesian approaches for flows in highly heterogeneous porous media and their applications N Guha, X Tan Journal of Computational and Applied Mathematics 317, 700-717, 2017 | 14 | 2017 |
| A conditional density estimation partition model using logistic Gaussian processes RD Payne, N Guha, Y Ding, BK Mallick Biometrika 107 (1), 173-190, 2020 | 10 | 2020 |
| Optimal transport based tracking of space objects in cylindrical manifolds N Das, RP Ghosh, N Guha, R Bhattacharya, B Mallick The Journal of the Astronautical Sciences 66 (4), 582-606, 2019 | 10 | 2019 |
| Quantile graphical models: a Bayesian approach N Guha, V Baladandayuthapani, BK Mallick Journal of Machine Learning Research 21 (79), 1-47, 2020 | 8* | 2020 |
| Bayesian multiscale finite element methods. modeling missing subgrid information probabilistically Y Efendiev, WT Leung, SW Cheung, N Guha, VH Hoang, B Mallick International Journal for Multiscale Computational Engineering 15 (2), 2017 | 6 | 2017 |
| Bayesian variable selection in multivariate nonlinear regression with graph structures Y Niu, N Guha, D De, A Bhadra, V Baladandayuthapani, BK Mallick arXiv preprint arXiv:2010.14638, 2020 | 4 | 2020 |
| Dynamic data-driven bayesian gmsfem SW Cheung, N Guha Journal of Computational and Applied Mathematics 353, 72-85, 2019 | 4 | 2019 |
| Comment on Article by Chkrebtii, Campbell, Calderhead, and Girolami BK Mallick, K Yang, N Guha, Y Efendiev | 3 | 2016 |
| A Bayesian Survival Tree Partition Model Using Latent Gaussian Processes RD Payne, N Guha, BK Mallick arXiv preprint arXiv:2207.03242, 2022 | 1 | 2022 |
| A Bayesian survival treed hazards model using latent Gaussian processes RD Payne, N Guha, BK Mallick Biometrics 80 (1), ujad009, 2024 | | 2024 |
| Stochastic Variance-Reduced Majorization-Minimization Algorithms DN Phan, S Bartz, N Guha, HM Phan arXiv preprint arXiv:2305.06848, 2023 | | 2023 |
| On Posterior consistency of Bayesian Changepoint models N Guha, J Datta arXiv preprint arXiv:2102.12938, 2021 | | 2021 |
| Stochastic Approximation Algorithm for Estimating Mixing Distribution for Dependent Observations N Guha, A Roy arXiv preprint arXiv:2006.14734, 2020 | | 2020 |
| Dynamic Data-driven Bayesian GMsFEM S Wun Cheung, N Guha arXiv e-prints, arXiv: 1806.05832, 2018 | | 2018 |
| Bayesian Solution Uncertainty Quantification for Differential Equations BK Mallick, K Yang, N Guha, Y Efendiev BAYESIAN ANALYSIS 11 (4), 1279-1284, 2016 | | 2016 |
| AN OPTIMAL SHRINKAGE FACTOR IN PREDICTION OF ORDERED RANDOM EFFECTS N Guha, A Roy, Y Malinovsky, G Datta Statistica Sinica, 1709-1728, 2016 | | 2016 |
| Bayesian Estimation Under Shape Restriction and Some Deconvolution Problems N Guha University of Maryland, Baltimore County, 2014 | | 2014 |
