Smoothness of Metropolis-Hastings algorithm and application to entropy estimation
نویسندگان
چکیده
منابع مشابه
The Metropolis-Hastings-Green Algorithm
1.1 Dimension Changing The Metropolis-Hastings-Green algorithm (as opposed to just MetropolisHastings with no Green) is useful for simulating probability distributions that are a mixture of distributions having supports of different dimension. An early example (predating Green’s general formulation) was an MCMC algorithm for simulating spatial point processes (Geyer and Møller, 1994). More wide...
متن کاملUnderstanding the Metropolis-Hastings Algorithm
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. 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 only for your perso...
متن کاملOn a Directionally Adjusted Metropolis-Hastings Algorithm
We propose a new Metropolis-Hastings algorithm for sampling from smooth, unimodal distributions; a restriction to the method is that the target be optimizable. The method can be viewed as a mixture of two types of MCMC algorithm; specifically, we seek to combine the versatility of the random walk Metropolis and the efficiency of the independence sampler as found with various types of target dis...
متن کاملImproving on the Independent Metropolis-Hastings Algorithm
This paper proposes methods to improve Monte Carlo estimates when the Independent MetropolisHastings Algorithm (IMHA) is used. Our rst approach uses a control variate based on the sample generated by the proposal distribution. We derive the variance of our estimator for a xed sample size n and show that, as n tends to in nity, this variance is asymptotically smaller than the one obtained with t...
متن کاملModified Metropolis-Hastings algorithm with Delayed Rejection
The development of an efficient MCMC strategy for sampling from complex distributions is a difficult task that needs to be solved for calculating small failure probabilities encountered in high-dimensional reliability analysis of engineering systems. Usually different variations of the Metropolis-Hastings algorithm (MH) are used. However, the standard MH algorithm does generally not work in hig...
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: ESAIM: Probability and Statistics
سال: 2013
ISSN: 1292-8100,1262-3318
DOI: 10.1051/ps/2012004