We consider Bayesian nonparametric inference in the right-censoring survival model, where modeling is made at level of hazard rate. derive posterior limiting distributions for linear functionals hazard, and then ‘many’ simultaneously appropriate multiscale spaces. As an application, we Bernstein–von Mises theorems cumulative functions, which lead to asymptotically efficient confidence bands the...

abstract background: lung cancer is an important cause of cancer mortality. mortality is a familiar projection to address the burden of cancers. however, according to iranian death registry, about 20% of death statistics were still recorded in misclassified categories. the aim of this study was to estimate lung cancer mortality in iranian population, using bayesian approach to revise this miscl...

the planning of water resources is based on the volume of water extracted from the aquifer and accurate estimate of this volume considerably helps to development. in this study, the bayesian networks model using continues and clustering structures was used to simulate the groundwater level of birjand aquifer. bayesian networks was calibrated with five input variables of aquifer recharge, water ...

I consider the development of Markov chain Monte Carlo (MCMC) methods, from late-1980s Gibbs sampling to present-day gradient-based methods and piecewise-deterministic processes. In parallel, show how these ideas have been implemented in successive generations statistical software for Bayesian inference. These packages instrumental popularizing applied modeling across a wide variety scientific ...

We harness the power of Bayesian emulation techniques, designed to aid analysis complex computer models, examine structure analyses themselves. These techniques facilitate robust and/or sensitivity problems, and hence allow global exploration impacts choices made in both likelihood prior specification. show how previously intractable problems robustness studies can be overcome using these metho...