Bayesian Inference for the Mover-Stayer Model of Continuous Time
This paper presents bayesian inference procedures for the continuous time mover-stayer model applied to individual transition data collected in discrete time. In particular, these methods allow to derive the probability of embeddability of the discrete-time modelling with the continuous-time one. A special emphasis is put on two alternative procedures, namely the importance of sampling and Gibbs sampling algorithms.
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|Date of creation:||1998|
|Date of revision:|
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