Bayesian Model Discrimination and Bayes Factors for Linear Gaussian State Space Models
本文展示了如何用贝叶斯方法区分不同的线性高斯状态空间模型，通过最小化期望损失选择模型，并利用马尔可夫链蒙特卡洛方法实现完全贝叶斯分析，适用于参数边界假设检验、非嵌套模型判别等多模型场景。
SUMMARY It is shown how to discriminate between different linear Gaussian state space models for a given time series by means of a Bayesian approach which chooses the model that minimizes the expected loss. A practical implementation of this procedure requires a fully Bayesian analysis for both the state vector and the unknown hyperparameters and is carried out by Markov chain Monte Carlo methods. An application to some non-standard situations such as testing hypotheses on the boundary of the parameter space, discriminating non-nested models and discrimination of more than two models is discussed in detail.