Honest Exploration of Intra table Probability Distributions Via Markov Chain Monte Carlo

نویسندگان

  • Galin L. Jones
  • James P. Hobert
چکیده

برای دانلود رایگان 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

Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models

SUMMARY The Bayesian approach to comparing models involves calculating the posterior probability of each plausible model. For high-dimensional contingency tables, the set of plausible models is very large. We focus attention on reversible jump Markov chain Monte Carlo (Green, 1995) and develop strategies for calculating posterior probabilities of hierarchical, graphical or decomposable log-line...

متن کامل

Computational and Inferential Diiculties with Mixture Posterior Distributions 1

This paper deals with both exploration and interpretation problems related to posterior distributions for mixture models. The speciication of mixture posterior distributions means that the presence of k! modes is known immediately. Standard Markov chain Monte Carlo techniques usually have diiculties with well-separated modes such as occur here; the Markov chain Monte Carlo sampler stays within ...

متن کامل

Sequential Monte Carlo for Graphical Models

We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a monotonically increasing sequence of probability spaces. By targeting these auxiliary distributions using SMC we are able to approximate the full joint distrib...

متن کامل

Markov chain Monte Carlo methods for visual tracking

Tracking articulated figures in high dimensional state spaces require tractable methods for inferring posterior distributions of joint locations, angles, and occlusion parameters. Markov chain Monte Carlo (MCMC) methods are efficient sampling procedures for approximating probability distributions. We apply MCMC to the domain of people tracking and investigate a general framework for sample-appr...

متن کامل

Small-world Mcmc and Convergence to Multi-modal Distributions: from Slow Mixing to Fast Mixing

Department of Mathematics, University of Idaho We compare convergence rates of Metropolis–Hastings chains to multi-modal target distributions when the proposal distributions can be of “local” and “small world” type. In particular, we show that by adding occasional long-range jumps to a given local proposal distribution, one can turn a chain that is “slowly mixing” (in the complexity of the prob...

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

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2000