Monte Carlo methods for approximating a posterior hazard rate process

✍ Scribed by P. W. Laud; A. F. M. Smith; P. Damien

• Publisher: Springer US
• Year: 1996
• Tongue: English
• Weight: 620 KB
• Volume: 6
• Category: Article
• ISSN: 0960-3174
• DOI: 10.1007/bf00161576

✦ Synopsis

In the context of Bayesian non-parametric statistics, the distribution of a stochastic process serves as a prior over the class of functions indexed by its sample paths. Dykstra and Laud (1981) defined a stochastic process whose sample paths can be used to index monotone hazard rates. Although they gave a mathematical description of the corresponding posterior process, numerical evaluations of useful posterior summaries were not feasible for realistic sample sizes.

Here we show how a full Bayesian posterior computation is made possible by novel Monte Carlo methods that approximate random increments of the posterior process.