Bayesian estimation of state-space models using the Metropolis–Hastings algorithm within Gibbs sampling
- 1 August 2001
- journal article
- Published by Elsevier in Computational Statistics & Data Analysis
- Vol. 37 (2) , 151-170
- https://doi.org/10.1016/s0167-9473(01)00009-3
Abstract
No abstract availableThis publication has 30 references indexed in Scilit:
- On markov chain monte carlo methods for nonlinear and non-gaussian state-space modelsCommunications in Statistics - Simulation and Computation, 1999
- Nonlinear and non-Gaussian state-space modeling with Monte Carlo simulationsJournal of Econometrics, 1998
- Markov chain Monte Carlo in conditionally Gaussian state space modelsBiometrika, 1996
- Nonlinear filters based on taylor series expansions∗Communications in Statistics - Theory and Methods, 1996
- Markov Chains for Exploring Posterior DistributionsThe Annals of Statistics, 1994
- Prediction, filtering and smoothing in non‐linear and non‐normal cases using Monte Carlo integrationJournal of Applied Econometrics, 1994
- A Monte Carlo Approach to Nonnormal and Nonlinear State-Space ModelingJournal of the American Statistical Association, 1992
- Sampling-Based Approaches to Calculating Marginal DensitiesJournal of the American Statistical Association, 1990
- Non-Gaussian State-Space Modeling of Nonstationary Time SeriesJournal of the American Statistical Association, 1987
- The Calculation of Posterior Distributions by Data AugmentationJournal of the American Statistical Association, 1987