| Computational neuroimaging strategies for single patient predictions KE Stephan, F Schlagenhauf, QJM Huys, S Raman, EA Aponte, ... Neuroimage 145, 180-199, 2017 | 186 | 2017 |
| A hemodynamic model for layered BOLD signals J Heinzle, PJ Koopmans, HEM den Ouden, S Raman, KE Stephan Neuroimage 125, 556-570, 2016 | 163 | 2016 |
| TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry S Frässle, EA Aponte, S Bollmann, KH Brodersen, CT Do, OK Harrison, ... Frontiers in Psychiatry 12, 857, 2021 | 156 | 2021 |
| The Bayesian group-lasso for analyzing contingency tables S Raman, TJ Fuchs, PJ Wild, E Dahl, V Roth Proceedings of the 26th Annual International Conference on Machine Learning …, 2009 | 128 | 2009 |
| mpdcm: A toolbox for massively parallel dynamic causal modeling EA Aponte, S Raman, B Sengupta, WD Penny, KE Stephan, J Heinzle Journal of neuroscience methods 257, 7-16, 2016 | 39 | 2016 |
| A hierarchical model for integrating unsupervised generative embedding and empirical Bayes S Raman, L Deserno, F Schlagenhauf, KE Stephan Journal of neuroscience methods 269, 6-20, 2016 | 21 | 2016 |
| Automatic model selection in archetype analysis S Prabhakaran, S Raman, JE Vogt, V Roth Pattern Recognition: Joint 34th DAGM and 36th OAGM Symposium, Graz, Austria …, 2012 | 18 | 2012 |
| Infinite mixture-of-experts model for sparse survival regression with application to breast cancer S Raman, TJ Fuchs, PJ Wild, E Dahl, JM Buhmann, V Roth BMC bioinformatics 11 (8), S8, 2010 | 17 | 2010 |
| Variational Bayesian inversion for hierarchical unsupervised generative embedding (HUGE) Y Yao, SS Raman, M Schiek, A Leff, S Frässle, KE Stephan NeuroImage 179, 604-619, 2018 | 13 | 2018 |
| An introduction to thermodynamic integration and application to dynamic causal models EA Aponte, Y Yao, S Raman, S Frässle, J Heinzle, WD Penny, ... Cognitive Neurodynamics, 1-15, 2021 | 12 | 2021 |
| Sparse Bayesian regression for grouped variables in generalized linear models S Raman, V Roth Joint Pattern Recognition Symposium, 242-251, 2009 | 8 | 2009 |
| Probabilistic clustering of time-evolving distance data JE Vogt, M Kloft, S Stark, SS Raman, S Prabhakaran, V Roth, G Rätsch Machine learning 100 (2), 635-654, 2015 | 6 | 2015 |
| Bayesian Grouped Variable Selection SS Raman Ph. D. Thesis, Computer Science Department, University of Basel, Switzerland, 2012 | 2 | 2012 |
| Serotonin Reduces Belief Stickiness VA Conceição, FH Petzschner, DM Cole, KV Wellstein, D Müller, ... bioRxiv, 2023.12. 08.570769, 2023 | 1 | 2023 |
| Sparse point estimation for bayesian regression via simulated annealing S Raman, V Roth Pattern Recognition, 317-326, 2012 | 1 | 2012 |
| Thermodynamic integration for dynamic causal models EA Aponte, S Raman, S Frässle, J Heinzle, WD Penny, KE Stephan bioRxiv, 471417, 2018 | | 2018 |
| Bayesian Survival Models for Analyzing Breast Tumors V Roth, TJ Fuchs, SS Raman, PJ Wild NIPS Workshop, 2008 | | 2008 |
| Bayesian Inference SS Raman, TN Unit | | |
| Bayesian Model Selection SS Raman, TN Unit | | |
| MAP Estimation via Simulated Annealing for Sparse Bayesian Regression using MCMC sampling S Raman, V Roth | | |
Klaas Enno StephanTranslational Neuromodeling Unit (TNU), IBT, University of Zurich and ETH ZurichVerified email at uzh.ch
