| Consistency of Bayesian inference with Gaussian process priors in an elliptic inverse problem M Giordano, R Nickl Inverse Problems 36 (8), 085001, 2020 | 61 | 2020 |
| Bernstein--von Mises Theorems and Uncertainty Quantification for Linear Inverse Problems M Giordano, H Kekkonen SIAM/ASA Journal on Uncertainty Quantification 8 (1), 342-373, 2020 | 18 | 2020 |
| Nonparametric Bayesian inference for reversible multidimensional diffusions M Giordano, K Ray The Annals of Statistics 50 (5), 2872-2898, 2022 | 15 | 2022 |
| On the inability of Gaussian process regression to optimally learn compositional functions M Giordano, K Ray, J Schmidt-Hieber Advances in Neural Information Processing Systems 35, 22341-22353, 2022 | 11 | 2022 |
| Besov-Laplace priors in density estimation: optimal posterior contraction rates and adaptation M Giordano Electronic Journal of Statistics 17 (2), 2210-2249, 2023 | 2 | 2023 |
| Asymptotic theory for Bayesian nonparametric inference in statistical models arising from partial differential equations M Giordano | 1 | 2021 |
| A Bayesian approach with Gaussian priors to the inverse problem of source identification in elliptic PDEs M Giordano arXiv preprint arXiv:2402.19214, 2024 | | 2024 |
| Nonparametric Bayesian intensity estimation for covariate-driven inhomogeneous point processes M Giordano, A Kirichenko, J Rousseau arXiv preprint arXiv:2312.14073, 2023 | | 2023 |
| Bayesian nonparametric inference in PDE models: asymptotic theory and implementation M Giordano 2023 JSM Proceedings, 1-17, 2023 | | 2023 |
| A reversible allelic partition process and Pitman sampling formula M Giordano, P De Blasi, M Ruggiero ALEA, Lat. Am. J. Probab. Math. Stat. 17, 375–388, 2020 | | 2020 |
Richard NicklUniversity of CambridgeBestätigte E-Mail-Adresse bei statslab.cam.ac.uk
