论文标题
贝叶斯共识：在异性噪声下估计错误校准的仪器的共识估计
Bayesian Consensus: Consensus Estimates from Miscalibrated Instruments under Heteroscedastic Noise
论文作者
论文摘要
我们考虑了一组人类预测者，模型，传感器或其他工具的预测或测量的问题，这些问题可能会受到偏见或错误校准以及随机异质噪声的影响。我们提出了一个贝叶斯共识估计器，该估计值调整了错误的校准和噪声，并表明该估计器是公正的，渐近地比幼稚的替代方案更有效。我们进一步提出了一个层次的贝叶斯模型，该模型利用我们提出的估计器，并将其应用于两个现实世界的预测挑战，这些挑战需要从易于误差的个人估计中获得共识估计：预测诸如疾病（ILI）每周百分比（ILI）每周百分比和预测上市公司的年收入。我们证明我们的方法可有效缓解偏差和错误，并与现有共识模型相比，导致更准确的预测。
We consider the problem of aggregating predictions or measurements from a set of human forecasters, models, sensors or other instruments which may be subject to bias or miscalibration and random heteroscedastic noise. We propose a Bayesian consensus estimator that adjusts for miscalibration and noise and show that this estimator is unbiased and asymptotically more efficient than naive alternatives. We further propose a Hierarchical Bayesian Model that leverages our proposed estimator and apply it to two real world forecasting challenges that require consensus estimates from error prone individual estimates: forecasting influenza like illness (ILI) weekly percentages and forecasting annual earnings of public companies. We demonstrate that our approach is effective at mitigating bias and error and results in more accurate forecasts than existing consensus models.
