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用于稳健高效风险评估的部分删失后验AI 解读

A novel approach to inference for a specific region of the predictive distribution is introduced. An important domain of application is accurate prediction of financial risk measures, where the area of interest is the left tail of the predictive density of logreturns. Our proposed approach originates from the Bayesian approach to parameter estimation and time series forecasting, however it is robust in the sense that it provides a more accurate estimation of the predictive…

非线性非高斯状态空间模型中参数与状态的联合贝叶斯分析AI 解读

Summary We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear, non‐Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent Metropolis–Hastings algorithm or in importance sampling. Our method provides a computationally more efficient alternative to several recently proposed algorithms. We present…

用于后验与预测模拟的自适应重要性加权 EM 算法AI 解读

A class of adaptive sampling methods is introduced for efficient posterior and predictive simulation. The proposed methods are robust in the sense that they can handle target distributions that exhibit non-elliptical shapes such as multimodality and skewness. The basic method makes use of sequences of importance weighted Expectation Maximization steps in order to efficiently construct a mixture of Student-t densities that approximates accurately the target…

评论：平方误差损失下的预测理性AI 解读

Forecast rationality under squared error loss implies various bounds on second moments of the forecasts across different horizons. For example, the mean squared forecast error should be nondecreasi...

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