Using the Bayesian Shtarkov solution for predictions
本文定义并应用贝叶斯Shtarkov预测器处理难以精细建模的数据集，计算表明其预测误差优于多种通用预测方法，如加性模型、装袋或堆叠支持向量机等。
The Bayes Shtarkov predictor can be defined and used for a variety of data sets that are exceedingly hard if not impossible to model in any detailed fashion. Indeed, this is the setting in which the derivation of the Shtarkov solution is most compelling. The computations show that anytime the numerical approximation to the Shtarkov solution is ‘reasonable’, it is better in terms of predictive error than a variety of other general predictive procedures. These include two forms of additive model as well as bagging or stacking with support vector machines, Nadaraya–Watson estimators, or draws from a Gaussian Process Prior.