About this Jupyter Book

About these lecture notes

These lecture notes have been authored by Dick Furnstahl and are released under a Creative Commons BY-NC license. The book format is powered by Jupyter Book.

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These notes originated from an intensive three-week summer school course taught at the University of York in 2019 by Christian Forssén, Dick Furnstahl, and Daniel Phillips as part of the TALENT initiative. The first version of the notes were used to teach a graduate course at The Ohio State University in Autumn, 2019. The original notes and subsequent revisions have been informed by interactions with many colleagues; I am particularly grateful to:

  • Prof. Christian Forssén, Chalmers

  • Prof. Morten Hjorth-Jensen, Oslo University and Michigan State University

  • Dr. Jordan Melendez, Ohio State University and Root Insurance

  • Prof. Daniel Phillips, Ohio University

  • Prof. Matt Pratola, Ohio State University

  • Prof. Sarah Wesolowski, Salisbury University

The full list of people that have contributed with ideas, discussions, or by generously sharing their knowledge is very long. Rather than inadvertently omitting someone, I simply say thank you to all. More generally, I am truly thankful for being part of an academic environment in which ideas and efforts are shared rather than kept isolated. The last statement extends to the open-source communities that make so many wonderful computing tools publicly available. In this course we take great advantage of open-source Python libraries.

The development of this course also would not have been possible without the knowledge gained through the study of several excellent textbooks, most of which are listed as recommended course literature. Here is a short list of those references that I have found particularly useful as a physicist learning Bayesian statistics and machine learning:

[GCS+13] Andrew Gelman et al., “Bayesian Data Analysis, Third Edition”, Chapman & Hall/CRC Texts in Statistical Science (2013).
[Gre05] Phil Gregory, “Bayesian Logical Data Analysis for the Physical Sciences”, Cambridge University Press (2005).
[Jay03] E. T. Jaynes, “Probability Theory: The Logic of Science”, Cambridge University Press (2003).
[Mac03] David J.C. MacKay, “Information Theory, Inference, and Learning Algorithms”, Cambridge University Press (2005).
[SS06] D.S. Sivia with J. Skilling, “Data Analysis : A Bayesian Tutorial”, Oxford University Press (2006).

The presentation and examples in the book by Sivia have been the underlying guide for much of the course. Other valuable references can be found in the Bibliography.

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