A General Bayesian Discrete Time Survival Model (King, CPP)

Abstract: “We present a general Bayesian statistical model for discrete time, discrete state space stochastic processes. Applications include the modeling of recurrent and episodic disease processes, such as episodes of illicit drug use, as well as social processes such as educational enrollment and employment. We also present Markov chain Monte Carlo inference algorithms for our model, along with a freely available software package called “brea” which implements these methods in the R programming language.”