期刊名称：International Journal of Statistics and Probability
印刷版ISSN：1927-7032
电子版ISSN：1927-7040
出版年度：2013
卷号：2
期号：4
页码：22
DOI：10.5539/ijsp.v2n4p22
出版社：Canadian Center of Science and Education
摘要：This paper concerns the estimation of parameters in the ``Vasicek Interest Rate'' model under a Bayesian framework. These popular models are challenging to fit with Markov chain Monte Carlo (McMC) methods as the structure of the model leads to considerable autocorrelation in the chains. Accordingly, we demonstrate that a simple re-parameterisation using the Cholesky decomposition can greatly improves the performance of the McMC algorithm and hence lead to valid Bayesian inference on the Vasicek model.
