On the Optimal Transition Matrix for Markov Chain Monte Carlo Sampling

نویسندگان

  • Ting-Li Chen
  • Wei-Kuo Chen
  • Chii-Ruey Hwang
  • Hui-Ming Pai
چکیده

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

Convergence Analysis of Markov Chain Monte Carlo Linear Solvers Using Ulam-von Neumann Algorithm

The convergence of Markov chain–based Monte Carlo linear solvers using the Ulam– von Neumann algorithm for a linear system of the form x = Hx + b is investigated in this paper. We analyze the convergence of the Monte Carlo solver based on the original Ulam–von Neumann algorithm under the conditions that ‖H‖ < 1 as well as ρ(H) < 1, where ρ(H) is the spectral radius of H. We find that although t...

متن کامل

Constructing Optimal Transition Matrix Formarkov Chain Monte Carlo via Global Optimization

The notion of asymptotic variance has been used as a means for performance evaluation of MCMCmethods. An imperative task when constructing a Markov chain for the Monte Carlo simulation with prescribed stationary distribution is to optimize the asymptotic variance. Cast against an appropriately chosen coordinate system, the worst-case analysis of the asymptotic variance arising in MCMC can be fo...

متن کامل

On Markov Chain Monte Carlo Methods for Nonlinear and Non-gaussian State-space Models

In this paper, a nonlinear and/or non-Gaussian smoother utilizing Markov chain Monte Carlo Methods is proposed, where the measurement and transition equations are specified in any general formulation and the error terms in the state-space model are not necessarily normal. The random draws are directly generated from the smoothing densities. For random number generation, the Metropolis-Hastings ...

متن کامل

Nonlinear and Non-gaussian State Estimation: a Quasi-optimal Estimator

The rejection sampling filter and smoother, proposed by Tanizaki (1996, 1999), Tanizaki and Mariano (1998) and Hürzeler and Künsch (1998), take a lot of time computationally. The Markov chain Monte Carlo smoother, developed by Carlin, Polson and Stoffer (1992), Carter and Kohn (1994, 1996) and Geweke and Tanizaki (1999a, 1999b), does not show a good performance depending on nonlinearity and non...

متن کامل

General Over-relaxation Markov Chain Monte Carlo Algorithms for Gaussian Densities

We study general over-relaxation Markov chain Monte Carlo samplers for multivariate Gaussian densities. We provide conditions for convergence based on the spectral radius of the transition matrix and on detailed balance. We illustrate these algorithms using an image analysis example.

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:
  • SIAM J. Control and Optimization

دوره 50  شماره 

صفحات  -

تاریخ انتشار 2012