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Learning Bayesian Models with R by Dr. Hari M. Koduvely

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About the Reviewers

Philip B. Graff is a data scientist with the Johns Hopkins University Applied Physics Laboratory. He works with graph analytics for a large-scale automated pattern discovery.

Philip obtained his PhD in physics from the University of Cambridge on a Gates Cambridge Scholarship, and a BS in physics and mathematics from the University of Maryland, Baltimore County. His PhD thesis implemented Bayesian methods for gravitational wave detection and the training of neural networks for machine learning.

Philip's post-doctoral research at NASA Goddard Space Flight Center and the University of Maryland, College Park, applied Bayesian inference to the detection and measurement of gravitational waves by ground and space-based detectors, LIGO ...

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