Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way...
This paper studies the input design problem for system identification where time domain constraints have to be considered. A finite Markov chain is used to model the input of the s...
Background: Many protein sequences are still poorly annotated. Functional characterization of a protein is often improved by the identification of its interaction partners. Here, ...
Andreas Henschel, Christof Winter, Wan Kyu Kim, Mi...
This work casts the traffic analysis of anonymity systems, and in particular mix networks, in the context of Bayesian inference. A generative probabilistic model of mix network ar...
This paper proposes a new randomized strategy for adaptive MCMC using Bayesian optimization. This approach applies to nondifferentiable objective functions and trades off explor...