Ville Satopää
Ville Satopää
Associate Professor, INSEAD
Verified email at - Homepage
Cited by
Cited by
Finding a" kneedle" in a haystack: Detecting knee points in system behavior
V Satopaa, J Albrecht, D Irwin, B Raghavan
2011 31st international conference on distributed computing systems …, 2011
Combining multiple probability predictions using a simple logit model
VA Satopää, J Baron, DP Foster, BA Mellers, PE Tetlock, LH Ungar
International Journal of Forecasting 30 (2), 344-356, 2014
The good judgment project: A large scale test of different methods of combining expert predictions
L Ungar, B Mellers, V Satopää, P Tetlock, J Baron
2012 AAAI Fall Symposium Series, 2012
Modeling probability forecasts via information diversity
VA Satopää, R Pemantle, LH Ungar
Journal of the American Statistical Association 111 (516), 1623-1633, 2016
Bias, information, noise: The BIN model of forecasting
VA Satopää, M Salikhov, PE Tetlock, B Mellers
Management Science 67 (12), 7599-7618, 2021
Mortality rate estimation and standardization for public reporting: Medicare’s hospital compare
EI George, V Ročková, PR Rosenbaum, VA Satopää, JH Silber
Journal of the American Statistical Association 112 (519), 933-947, 2017
Probability aggregation in time-series: Dynamic hierarchical modeling of sparse expert beliefs
VA Satopää, ST Jensen, BA Mellers, PE Tetlock, LH Ungar
Improving Medicare's hospital compare mortality model
JH Silber, VA Satopää, N Mukherjee, V Rockova, W Wang, AS Hill, ...
Health services research 51, 1229-1247, 2016
Boosting the wisdom of crowds within a single judgment problem: Weighted averaging based on peer predictions
AB Palley, VA Satopää
Management Science 69 (9), 5128-5146, 2023
Partial information framework: Model-based aggregation of estimates from diverse information sources
VA Satopää, ST Jensen, R Pemantle, LH Ungar
Regularized aggregation of one-off probability predictions
VA Satopää
Operations Research 70 (6), 3558-3580, 2022
Decomposing the effects of crowd-wisdom aggregators: The bias–information–noise (BIN) model
VA Satopää, M Salikhov, PE Tetlock, B Mellers
International Journal of Forecasting 39 (1), 470-485, 2023
Combining information from multiple forecasters: Inefficiency of central tendency
VA Satopää
arXiv preprint arXiv:1706.06006, 2017
Combining and extremizing real-valued forecasts
V Satopää, L Ungar
arXiv preprint arXiv:1506.06405, 2015
Bayesian aggregation of two forecasts in the partial information framework
P Ernst, R Pemantle, V Satopää, L Ungar
Statistics & Probability Letters 119, 170-180, 2016
Improving the wisdom of crowds with analysis of variance of predictions of related outcomes
VA Satopää
International Journal of Forecasting 37 (4), 1728-1747, 2021
Herding in probabilistic forecasts
Y Jia, J Keppo, V Satopää
Management Science 69 (5), 2713-2732, 2023
Simultaneous confidence intervals for comparing margins of multivariate binary data
B Klingenberg, V Satopää
Computational statistics & data analysis 64, 87-98, 2013
Joint bottom-up method for forecasting grouped time series: Application to Australian domestic tourism
N Bertani, V Satopää, S Jensen
Available at SSRN 3542278, 2021
Long‐range subjective‐probability forecasts of slow‐motion variables in world politics: Exploring limits on expert judgment
PE Tetlock, C Karvetski, VA Satopää, K Chen
Futures & Foresight Science 6 (1), e157, 2024
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