Maximum likelihood estimation for spatial models by Markov chain Monte Carlo stochastic approximation
β Scribed by Ming Gao Gu; Hong-Tu Zhu
- Book ID
- 108547597
- Publisher
- Blackwell Publishing
- Year
- 2001
- Tongue
- English
- Weight
- 317 KB
- Volume
- 63
- Category
- Article
- ISSN
- 0952-8385
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
To obtain the likelihood of a non-Gaussian state-space model, Durbin and Koopman (1997, Biometrika, 84, 669 -684) ΓΏrst calculate the likelihood under an approximating linear Gaussian model and then use Monte Carlo methods to estimate the necessary adjustment factor. We show that Durbin and Koopman's
The marked increase in popularity of Bayesian methods in statistical practice over the last decade owes much to the simultaneous development of Markov chain Monte Carlo (MCMC) methods for the evaluation of requisite posterior distributions. However, along with this increase in computing power has co