Bayesian Networks vs. Petersburg

A couple of weeks ago we walked through how petersburg represents complex decisions (check it out here). Some of you may have recognized a familiar concept in that description: Bayesian networks (or bayesnet).  Just like petersburg’s structure, a Bayesian network is at it’s core a Directed Acyclic Graph (DAG).  So let’s first discuss what a […]

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NetBSD support for psutil

Roughly two months have passed since I last announced psutil added support for OpenBSD platforms. Today I am happy to announce we also have NetBSD support! This was contributed by Thomas Klausner, Ryo Onodera and myself in PR #570. Differences with FreeBSD (and OpenBSD) NetBSD implementation has similar limitations as the ones I encountered with OpenBSD. Again, […]

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Behind the paper: Are visibility-derived AOT estimates suitable for parameterising satellite data atmospheric correction algorithms?

This has been a bit slow coming, but I am now sticking to my promise to write a Behind the paper post for each of my published academic papers. This is about: Wilson, R. T., E. J. Milton, and J. M. Nield (2015). Are visibility-derived AOT estimates suitable for parameterising satellite data atmospheric correction algorithms? International Journal of Remote […]

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What is Model-Based Machine Learning?

About Tom: Tom Diethe is a research fellow on the SPHERE project at the University of Bristol. His research interests include probabilistic machine learning, computational statistics, learning theory, and data fusion. He has a PhD in machine learning applied to multivariate signal processing from University College London. Contact him at tom.diethe@bristol.ac.uk. Introduction If you haven’t […]

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