Convergence Diagnostics for Markov Chain Monte Carlo
نویسندگان
چکیده
منابع مشابه
Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive o...
متن کاملTheoretical rates of convergence for Markov chain Monte Carlo
We present a general method for proving rigorous, a priori bounds on the number of iterations required to achieve convergence of Markov chain Monte Carlo. We describe bounds for specific models of the Gibbs sampler, which have been obtained from the general method. We discuss possibilities for obtaining bounds more generally.
متن کاملSearching for convergence in phylogenetic Markov chain Monte Carlo.
Markov chain Monte Carlo (MCMC) is a methodology that is gaining widespread use in the phylogenetics community and is central to phylogenetic software packages such as MrBayes. An important issue for users of MCMC methods is how to select appropriate values for adjustable parameters such as the length of the Markov chain or chains, the sampling density, the proposal mechanism, and, if Metropoli...
متن کاملConvergence assessment techniques for Markov chain Monte Carlo
While MCMC is one of the most widely used tools in advanced applied statistics, there is a consensus in the literature that the existing battery of convergence diagnostics is large but inadequate, especially for complicated chains. Methods based on exact results are often unwieldy in applications or badly overconservative; certain existing ad-hoc techniques are not sufficiently rigourous. Still...
متن کاملMinorization Conditions and Convergence Rates for Markov Chain Monte Carlo
We provide general methods for analyzing the convergence of discrete-time, general state space Markov chains, such as those used in stochastic simulation algorithms including the Gibbs sampler. The methods provide rigorous, a priori bounds on how long these simulations should be run to give satisfactory results. We apply our results to two models of the Gibbs sampler, the rst a bivariate normal...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Annual Review of Statistics and Its Application
سال: 2020
ISSN: 2326-8298,2326-831X
DOI: 10.1146/annurev-statistics-031219-041300