Abstract
A general approach to implement a specification syntax for Markov Chain Monte Carlo Methods, especially Gibbs sampling, is presented.
If one is able to formulate a statistical model in terms of full conditional distributions, a sampling scheme can be derived automatically. The software itself determines automatically the needed functions and data to run the sampling scheme. In this way, one can easily use the functionality of Gibbs sampling schemes without much knowledge of programming. By providing samples from the desired posterior distribution, all further analyses can be based on the sample.
On the other hand, one can also try to derive the sampling scheme automatically. In this case, the task of deriving the setup for obtaining the posterior sample is left to the program.
We present a specification language for time series modelling and demonstrate a flexible way of setting up a variety of models in this context. A first implementation with examples will be demonstrated.
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© 1994 Springer-Verlag Berlin Heidelberg
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Hartmann, P., Jin, S., Krause, A. (1994). A Specification Language for Gibbs Sampling. In: Dutter, R., Grossmann, W. (eds) Compstat. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-52463-9_45
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DOI: https://doi.org/10.1007/978-3-642-52463-9_45
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-0793-6
Online ISBN: 978-3-642-52463-9
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