Abstract
This paper reviews the Bayesian approach to parameter estimation in nonlinear nonnormal state-space models with posterior computations performed by Gibbs sampling. Fitting of nonlinear nonnormal state-space models is an important task in various scientific disciplines. The ease with which the Bayesian approach can now be implemented via BUGS, a recently developed, user-friendly, and freely available software package, should have a major impact on applied research. This is illustrated using examples from three different areas of currently active research: econonometrics, fisheries, and physics.
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Meyer, R. (2000). Applied Bayesian Data Analysis Using State-Space Models. In: Gaul, W., Opitz, O., Schader, M. (eds) Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58250-9_21
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DOI: https://doi.org/10.1007/978-3-642-58250-9_21
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