The Metropolis Algorithm
نویسندگان
چکیده
منابع مشابه
Understanding 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 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 کاملAn Adaptive Metropolis algorithm
A proper choice of a proposal distribution for MCMC methods, e.g. for the Metropolis-Hastings algorithm, is well known to be a crucial factor for the convergence of the algorithm. In this paper we introduce an adaptive Metropolis Algorithm (AM), where the Gaussian proposal distribution is updated along the process using the full information cumulated so far. Due to the adaptive nature of the pr...
متن کاملAn adaptive Metropolis algorithm
A proper choice of a proposal distribution for Markov chain Monte Carlo methods, for example for the Metropolis±Hastings algorithm, is well known to be a crucial factor for the convergence of the algorithm. In this paper we introduce an adaptive Metropolis (AM) algorithm, where the Gaussian proposal distribution is updated along the process using the full information cumulated so far. Due to th...
متن کاملOptimizing and Adapting the Metropolis Algorithm
Many modern scientific questions involve high-dimensional data and complicated statistical models. For example, data on weather consist of huge numbers of measurements across spatial grids, over a period of time. Even in simpler settings, data can be complex: for example, Bartolucci et al. (2007) consider recurrence rates for melanoma (skin cancer) patients after surgery. The probability of rec...
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: Computing in Science & Engineering
سال: 2000
ISSN: 1521-9615
DOI: 10.1109/5992.814660