Abstract
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.
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Laud, P.W., Smith, A.F.M. & Damien, P. Monte Carlo methods for approximating a posterior hazard rate process. Stat Comput 6, 77–83 (1996). https://doi.org/10.1007/BF00161576
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DOI: https://doi.org/10.1007/BF00161576