Abstract
An algorithm, called the scan sampler, is developed and discussed. The scan sampler has a variety of uses for time series analysis based on the state space model with nonGaussian observations. The algorithm is based on the Kaiman filter/smoothing algorithm. It can be used for Bayesian inference using Markov chain Monte Carlo and to find posterior modes.
Original language | English |
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Pages (from-to) | 929-937 |
Number of pages | 9 |
Journal | Biometrika |
Volume | 84 |
Issue number | 4 |
Publication status | Published - 1997 |
Externally published | Yes |
Keywords
- Exponential family
- Generalised linear time series
- Gibbs sampling
- Kaiman filter
- Markov chain monte carlo
- Nonparametric regression
- Smoothing filter
- State space model
- Stochastic volatility
- Tobit models