Background. The Georgetown University-Albert Einstein College of Medicine breast cancer
simulation model (Model GE) has evolved over time in structure and function to reflect …

With the shift toward individualized treatment, cost-effectiveness models need to incorporate
patient and tumor characteristics that may be relevant to treatment planning. In this study, we …

Hydra is an open-source, platform-neutral library for performing Markov Chain Monte Carlo.
It implements the logic of standard MCMC samplers within a framework designed to be easy …

Bayesian nonparametric cross-study validation of prediction methods Page 1 The Annals of
Applied Statistics 2015, Vol. 9, No. 1, 402–428 DOI: 10.1214/14-AOAS798 © Institute of …

Bayesian methods have become increasingly popular in modern statistical analysis and are
being applied to a broad spectrum of scientific fields and research areas. This paper …

While there have been few theoretical contributions on the Markov Chain Monte Carlo
(MCMC) methods in the past decade, current understanding and application of MCMC to the …

Medical researchers interested in temporal, multivariate measurements of complex diseases
have recently begun developing health state models, which divide the space of patient …

Markov chain Monte Carlo (MCMC) simulations are commonly employed for estimating
features of a target distribution, particularly for Bayesian inference. A fundamental challenge …

In this paper, we consider methods for generating draws of a binary random variable whose
expectation conditional on covariates follows a logistic regression model with known …

With the development of MCMC methods, Bayesian methods play a more and more
important role in model selection and statistical prediction. However, the sensitivity of the …