Belief networks, hidden Markov models, and Markov random fields: a unifying view
Pattern Recognition Letters - special issue on pattern recognition in practice V
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Graphical Models: Methods for Data Analysis and Mining
Graphical Models: Methods for Data Analysis and Mining
Constructing Bayesian networks for criminal profiling from limited data
Knowledge-Based Systems
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The development of automated methods for chemical synthesis as well as for chemical analysis has inundated chemistry with huge amounts of experimental data. To refine them into information, the field of chemoinformatics applies techniques from artificial intelligence, pattern recognition and machine learning. A key task concerning organic chemistry is structure elucidation. NMR spectra have become accessible at low expenses of time and sample size, they also are predictable with good precision, and they are directly related to structural properties of the molecule. So the classical approach of ranking structure candidates by comparison of NMR spectra works well, but since the structural space is huge, more sophisticated approaches are in demand. Bayesian networks are promising in this concern, as they allow for contemplation in a dual way: provided an appropriate model, conclusions can be drawn from a given spectrum regarding the corresponding structure or vice versa, since the same interrelations hold in both directions. The development of such a model is documented, and first results are shown supporting the applicability of Bayesian networks to structure elucidation.