Accession Number:

ADA279393

Title:

On Markov Chain Monte Carlo Acceleration

Descriptive Note:

Technical rept.

Corporate Author:

STANFORD UNIV CA DEPT OF STATISTICS

Personal Author(s):

• Gelfand, Alan E.
• Sahu, Sujit K.

Report Date:

1994-04-04

Pagination or Media Count:

24.0

Abstract:

Markov chain Monte Carlo MCMC methods are currently enjoying a surge of interest within the statistical community. The goal of this work is to formalize and support two distinct adaptive strategies which typically accelerate the convergence of a MCMC algorithm. One approach is through resampling the other incorporates adaptive switching of the transition kernel. Support is both by analytic arguments and simulation study. Application is envisioned in low dimensional but non-trivial problems. Two pathological illustrations are presented. Connections with reparametrization are discussed as well as possible difficulties with infinitely often adaptation.

Descriptors:

• *MONTE CARLO METHOD
• *MARKOV PROCESSES
• ALGORITHMS
• SIMULATION
• PROBABILITY DENSITY FUNCTIONS
• CONVERGENCE
• ADAPTATION
• SWITCHING
• TRANSITIONS
• KERNEL FUNCTIONS
• STRATEGY
• NUMERICAL ANALYSIS

Subject Categories:

• Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE