1. Data Augmentation algorithm for graphical models with missing data
- Creator:
- Kuroda, Masahiro
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Data augmentation algorithm, local computation, graphical models, computional efficiency, and missing data
- Language:
- English
- Description:
- In this paper, we discuss an efficient Bayesian computational method when observed data are incomplete in discrete graphical models. The data augmentation (DA) algorithm of Tanner and Wong [8] is applied to finding the posterior distribution. Utilizing the idea of local computation, it is possible to improve the DA algorithm. We propose a local computation DA (LC-DA) algorithm and evaluate its computational efficiency.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public