DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
- 1 March 1994
- journal article
- Published by Wiley in Journal of Time Series Analysis
- Vol. 15 (2) , 183-202
- https://doi.org/10.1111/j.1467-9892.1994.tb00184.x
Abstract
We define a subclass of dynamic linear models with unknown hyperpara‐meter calledd‐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.Keywords
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