Bayesian statistical learning provides a coherent probabilistic framework for modelling
uncertainty in systems. This review describes the theoretical foundations underlying …

Bayesian methods are valuable, inter alia, whenever there is a need to extract information
from data that are uncertain or subject to any kind of error or noise (including measurement …

Bayesian inference is a powerful statistical paradigm that has gained popularity in many
fields of science, but adoption has been somewhat slower in biophysics. Here, I provide an …

Inference in Bayesian networks | Nature Biotechnology Skip to main content Thank you for
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Bayesian methods are rapidly becoming popular tools for making statistical inference in
various fields of science including biology, engineering, finance, and genetics. One of the …

The Bayesian network is one of the most important architectures for representing and
reasoning with multivariate probability distributions. When used in conjunction with …

Background Computational models in biology are characterized by a large degree of
uncertainty. This uncertainty can be analyzed with Bayesian statistics, however, the …

Bayesian networks (BNs) provide a neat and compact representation for expressing joint
probability distributions (JPDs) and for inference. They are becoming increasingly important …

Almost all fields of science rely upon statistical inference to estimate unknown parameters in
theoretical and computational models. While the performance of modern computer hardware …

This volume discusses the development and application of Bayesian statistical methods for
the analysis of high-throughput bioinformatics data arising from problems in molecular and …