Abstract
ABSTRACTMarkov chain Monte Carlo (MCMC) algorithms are in wide use for fitting complicated statistical models in psychometrics in situations where the traditional estimation techniques are very difficult to apply. One of the stumbling blocks in using an MCMC algorithm is determining the convergence of the algorithm. Because the convergence is not that of a scalar quantity to a point, but that of a distribution to another distribution, the issue remains an enigma to many users of MCMC, especially to those without a sound knowledge of mathematical statistics. This article is an attempt to provide psychometricians using the MCMC algorithms a better understanding of the concept of convergence of the algorithms and an improved knowledge about the diagnostics tools to assess convergence of the MCMC algorithms.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
