33 citations · 44 across the 5 of their papers we have counts for
5 papers · 1 filter
Combining Information from Multiple Forecasters: Inefficiency of Central Tendency
Ville A. Satopää
Even though the forecasting literature agrees that aggregating multiple predictions of some future outcome typically outperforms the individual predictions, there is no general con…
Bayesian aggregation of two forecasts in the partial information framework
Philip Ernst, Robin Pemantle, Ville Satopaa +1
We generalize the results of \cite{SPU, SJPU} by showing how the Gaussian aggregator may be computed in a setting where parameter estimation is not required. We proceed to provide…
Combining and Extremizing Real-Valued Forecasts
Ville Satopää, Lyle Ungar
The weighted average is by far the most popular approach to combining multiple forecasts of some future outcome. This paper shows that both for probability or real-valued forecasts…
Partial Information Framework: Model-Based Aggregation of Estimates from Diverse Information Sources
Ville A. Satopää, Shane T. Jensen, Robin Pemantle +1
Prediction polling is an increasingly popular form of crowdsourcing in which multiple participants estimate the probability or magnitude of some future event. These estimates are t…
Modeling Probability Forecasts via Information Diversity
Ville A. Satopää, Robin Pemantle, Lyle H. Ungar
Randomness in scientific estimation is generally assumed to arise from unmeasured or uncontrolled factors. However, when combining subjective probability estimates, heterogeneity s…