As postgenomic biology becomes more predictive, the ability to infer rate parameters of genetic and biochemical networks will become increasingly important. In this paper, we expl...
In this paper, we propose a stochastic version of a general purpose functional programming language as a method of modeling stochastic processes. The language contains random choi...
The protein inference problem represents a major challenge in shotgun proteomics. Here we describe a novel Bayesian approach to address this challenge that incorporates the predict...
Yong Fuga Li, Randy J. Arnold, Yixue Li, Predrag R...
Abstract. This paper presents a Bayesian inference algorithm for image layer representation [26], 2.1D sketch [6], with mixed Markov random field. 2.1D sketch is an very important...
Background: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors f...
Bo Chen, Minhua Chen, John William Paisley, Aimee ...