A Geometric Interpretation of the Metropolis-Hastings Algorithm
نویسندگان
چکیده
منابع مشابه
A Geometric Interpretation of the Metropolis–Hastings Algorithm
The Metropolis-Hastings algorithm transforms a given stochastic matrix into a reversible stochastic matrix with a prescribed stationary distribution. We show that this transformation gives the minimum distance solution in an L1 metric.
متن کاملThe 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 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: Statistical Science
سال: 2001
ISSN: 0883-4237
DOI: 10.1214/ss/1015346318