October 2015
Beginner to intermediate
168 pages
4h 11m
English
Content preview from Learning Bayesian Models with RBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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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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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.