Bayesian Model Discrimination and Bayes Factors for Linear Gaussian State Space Models
Bayesian Model Discrimination and Bayes Factors for Linear Gaussian State Space Models
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线性高斯状态空间模型的贝叶斯模型判别和贝叶斯因子
DOI：
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发表时间：
1995
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影响因子：
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通讯作者：
S. Frühwirth
中科院分区：
文献类型：
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作者：
S. Frühwirth
总结它示出了如何区分不同的线性高斯状态空间模型，对于一个给定的时间序列，通过贝叶斯方法，选择的模型，最大限度地减少预期损失。这个过程的实际实现需要对状态向量和未知超参数进行完全贝叶斯分析，并通过马尔可夫链蒙特卡罗方法进行。并详细讨论了它在参数空间边界假设检验、非嵌套模型判别和两个以上模型判别等非标准情形中的应用。
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.