| Probabilistic programming: A review for environmental modellers C Krapu, M Borsuk Environmental Modelling & Software 114, 40-48, 2019 | 27 | 2019 |
| Identifying wetland consolidation using remote sensing in the North Dakota Prairie Pothole Region C Krapu, M Kumar, M Borsuk Water Resources Research 54 (10), 7478-7494, 2018 | 19 | 2018 |
| Gradient‐based inverse estimation for a rainfall‐runoff model C Krapu, M Borsuk, M Kumar Water Resources Research 55 (8), 6625-6639, 2019 | 12 | 2019 |
| A review of Bayesian networks for spatial data C Krapu, R Stewart, A Rose ACM Transactions on Spatial Algorithms and Systems 9 (1), 1-21, 2023 | 9 | 2023 |
| A spatial community regression approach to exploratory analysis of ecological data C Krapu, M Borsuk Methods in Ecology and Evolution 11 (5), 608-620, 2020 | 8 | 2020 |
| A Differentiable Hydrology Approach for Modeling With Time‐Varying Parameters C Krapu, M Borsuk Water Resources Research 58 (9), e2021WR031377, 2022 | 6 | 2022 |
| Synthesis and Characterization of Zinc-‐Oxide/Polystyrene Nanocomposite Thin Films C Krapu Macalester journal of physics and astronomy 1 (1), 7, 2013 | 3 | 2013 |
| Flexible hierarchical risk modeling for large insurance data via NumPyro C Krapu, M Borsuk arXiv preprint arXiv:2312.07432, 2023 | 1 | 2023 |
| Fluid hunter motivation in Central Africa: Effects on behaviour, bushmeat and income GZL Froese, A Ebang Mbélé, C Beirne, B Bazza, S Dzime N’noh, J Ebeba, ... People and Nature 5 (5), 1480-1496, 2023 | 1 | 2023 |
| A Bayesian model for multivariate discrete data using spatial and expert information with application to inferring building attributes C Krapu, N Hayes, R Stewart, K Kurte, A Rose, A Sorokine, M Urban Spatial Statistics 55, 100745, 2023 | 1 | 2023 |
| Development of Novel Bayesian Models of Environmental Systems with Application to the Prairie Wetlands of North America C Krapu Duke University, 2020 | 1 | 2020 |
| Deep autoregressive modeling for land use land cover C Krapu, M Borsuk, R Calder arXiv preprint arXiv:2401.01395, 2024 | | 2024 |
| Employing Gaussian process priors for studying spatial variation in the parameters of a cardiac action potential model AN Ramos, CL Krapu, EM Cherry, FH Fenton arXiv preprint arXiv:2311.10114, 2023 | | 2023 |
| Employing Gaussian process priors for studying spatial variation in the parameters of a cardiac action potential model A Nieto Ramos, CL Krapu, EM Cherry, FH Fenton arXiv e-prints, arXiv: 2311.10114, 2023 | | 2023 |
| A comparison of novel dynamic priors for Bayesian estimation of time-varying parameters in rainfall-runoff modeling via Hamiltonian Monte Carlo C Krapu Frontiers in Hydrology 2022, 400-04, 2022 | | 2022 |
| End-to-End Differentiable Modeling and Management of the Environment C Krapu, T Felgenhauer Artificial Intelligence for Earth System Predictability (AI4ESP …, 2021 | | 2021 |
| Bayesian estimation of a parsimonious wetland hydrology model with remote sensing data C Krapu, ME Borsuk, M Kumar AGU Fall Meeting Abstracts 2019, H31N-1949, 2019 | | 2019 |
| Forgotten Interactions: Missing Link in the Ecohydrologic Prediction Puzzle M Kumar, C Krapu, Y Liu, A Parolari, GG Katul, AM Porporato, ME Borsuk AGU Fall Meeting Abstracts 2019, H11J-1631, 2019 | | 2019 |
| Scalable Inverse Estimation with Variational Inference C Krapu, ME Borsuk AGU Fall Meeting Abstracts 2018, H21J-1774, 2018 | | 2018 |
| Efficient Inference for Mechanistic Models with Hamiltonian Monte Carlo C Krapu, M Borsuk | | 2018 |
