An Adaptive Exchange Algorithm for Sampling from Distributions with Intractable Normalizing Constants

نویسندگان

  • Faming Liang
  • Ick Hoon Jin
  • Qifan Song
  • Jun S. Liu
چکیده

An Adaptive Exchange Algorithm for Sampling from Distributions with Intractable Normalizing Constants Faming Liang, Ick Hoon Jin, Qifan Song & Jun S. Liu To cite this article: Faming Liang, Ick Hoon Jin, Qifan Song & Jun S. Liu (2015): An Adaptive Exchange Algorithm for Sampling from Distributions with Intractable Normalizing Constants, Journal of the American Statistical Association, DOI: 10.1080/01621459.2015.1009072 To link to this article: http://dx.doi.org/10.1080/01621459.2015.1009072

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Advances in Markov chain Monte Carlo methods

Probability distributions over many variables occur frequently in Bayesian inference, statistical physics and simulation studies. Samples from distributions give insight into their typical behavior and can allow approximation of any quantity of interest, such as expectations or normalizing constants. Markov chain Monte Carlo (MCMC), introduced by Metropolis et al. (1953), allows sampling from d...

متن کامل

A Monte Carlo Metropolis-Hastings Algorithm for Sampling from Distributions with Intractable Normalizing Constants

Simulating from distributions with intractable normalizing constants has been a long-standing problem in machine learning. In this letter, we propose a new algorithm, the Monte Carlo Metropolis-Hastings (MCMH) algorithm, for tackling this problem. The MCMH algorithm is a Monte Carlo version of the Metropolis-Hastings algorithm. It replaces the unknown normalizing constant ratio by a Monte Carlo...

متن کامل

Bayesian computation for statistical models with intractable normalizing constants

This paper deals with a computational aspect of the Bayesian analysis of statistical models with intractable normalizing constants. In the presence of intractable normalizing constants in the likelihood function, traditional MCMC methods cannot be applied. We propose here a general approach to sample from such posterior distributions that bypasses the computation of the normalizing constant. Ou...

متن کامل

Forward recursions and normalizing constant for Gibbs fields

Maximum likelihood parameter estimation is frequently replaced by various techniques because of its intractable normalizing constant. In the same way, the literature displays various alternatives for distributions involving such unreachable constants. In this paper, we consider a Gibbs distribution π and present a recurrence formula allowing a recursive calculus of the marginals of π and in the...

متن کامل

Monte Carlo Methods on Bayesian Analysis of Constrained Parameter Problems with Normalizing Constants

Constraints on the parameters in a Bayesian hierarchical model typically make Bayesian computation and analysis complicated. As Gelfand, Smith and Lee (1992) remarked, it is almost impossible to sample from a posterior distribution when its density contains analytically intractable integrals (normalizing constants) that depend on the (hyper) parameters. Therefore, the Gibbs sampler or the Metro...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2015