DATA AUGMENTATION AND DYNAMIC LINEAR MODELS

Authors


Abstract

Abstract. We define a subclass of dynamic linear models with unknown hyperpara-meter called d-inverse-gamma models. We then approximate the marginal probability density functions of the hyperparameter and the state vector by the data augmentation algorithm of Tanner and Wong. We prove that the regularity conditions for convergence hold. For practical implementation a forward-filtering-backward-sampling algorithm is suggested, and the relation to Gibbs sampling is discussed in detail.

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