Bayesian cluster analysis: Point estimation and credible balls (with discussion) S Wade, Z Ghahramani | 222 | 2018 |
Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics A Prestia, A Caroli, SK Wade, WM van der Flier, R Ossenkoppele, ... Alzheimer's & Dementia 11 (10), 1191-1201, 2015 | 101 | 2015 |
Improving prediction from Dirichlet process mixtures via enrichment S Wade, DB Dunson, S Petrone, L Trippa The Journal of Machine Learning Research 15 (1), 1041-1071, 2014 | 80 | 2014 |
An enriched conjugate prior for Bayesian nonparametric inference S Wade, S Mongelluzzo, S Petrone | 57 | 2011 |
Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences AK Leist, M Klee, JH Kim, DH Rehkopf, SPA Bordas, G Muniz-Terrera, ... Science Advances 8 (42), eabk1942, 2022 | 52 | 2022 |
Posterior inference for sparse hierarchical non-stationary models K Monterrubio-Gómez, L Roininen, S Wade, T Damoulas, M Girolami Computational Statistics & Data Analysis 148, 106954, 2020 | 36 | 2020 |
A Bayesian nonparametric regression model with normalized weights: A study of hippocampal atrophy in Alzheimer’s disease I Antoniano-Villalobos, S Wade, SG Walker Journal of the American Statistical Association 109 (506), 477-490, 2014 | 26 | 2014 |
A predictive study of Dirichlet process mixture models for curve fitting S Wade, SG Walker, S Petrone Scandinavian Journal of Statistics 41 (3), 580-605, 2014 | 25 | 2014 |
Bayesian cluster analysis S Wade Philosophical Transactions of the Royal Society A 381 (2247), 20220149, 2023 | 19 | 2023 |
Enriched mixtures of generalised Gaussian process experts C Gadd, S Wade, A Boukouvalas International Conference on Artificial Intelligence and Statistics, 3144-3154, 2020 | 18* | 2020 |
Alzheimer disease biomarkers as outcome measures for clinical trials in MCI A Caroli, A Prestia, S Wade, K Chen, N Ayutyanont, SM Landau, ... Alzheimer Disease & Associated Disorders 29 (2), 101-109, 2015 | 16 | 2015 |
Pseudo-marginal Bayesian inference for Gaussian process latent variable models C Gadd, S Wade, AA Shah Machine Learning 110, 1105-1143, 2021 | 8* | 2021 |
mcclust. ext: Point estimation and credible balls for Bayesian cluster analysis S Wade URL https://www. researchgate. net/publication/279848500 mcclustext-manual …, 2015 | 7 | 2015 |
Ultra-fast Deep Mixtures of Gaussian Process Experts C Etienam, K Law, S Wade arXiv preprint arXiv:2006.13309, 2020 | 6 | 2020 |
Bayesian nonparametric regression through mixture models S Wade Università Bocconi, 2013 | 6 | 2013 |
Leveraging variational autoencoders for multiple data imputation B Roskams-Hieter, J Wells, S Wade Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 5 | 2023 |
Mixtures of Gaussian Process Experts with SMC T Härkönen, S Wade, K Law, L Roininen arXiv preprint arXiv:2208.12830, 2022 | 5 | 2022 |
Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics. Alzheimers Dement. 2015. S1552-5260 … A Prestia, A Caroli, SK Wade, WM van der Flier, R Ossenkoppele, BB Van JAMA J Am Med Assoc 302, 2593-4, 2009 | 5 | 2009 |
Shared differential clustering across single-cell RNA sequencing datasets with the hierarchical Dirichlet process J Liu, S Wade, N Bochkina Econometrics and Statistics, 2024 | 4 | 2024 |
Nonstationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data W Yu, S Wade, HD Bondell, L Azizi Journal of Computational and Graphical Statistics 32 (2), 588-600, 2023 | 4 | 2023 |