References

[Acq23]

Viviana Acquaviva. Machine learning for physics and astronomy. Princeton University Press, 2023. (Chapter 1 available at https://press.princeton.edu/books/hardcover/9780691203928/machine-learning-for-physics-and-astronomy#preview).

[BS17]

Joseph F. Boudreau and Eric S. Swanson. Applied Computational Physics. Oxford University Press, 2017. (Direct link for U of Utah access: https://academic-oup-com.ezproxy.lib.utah.edu/book/26392). doi:10.1093/oso/9780198708636.001.0001.

[CT14]

João Paulo Casquilho and Paulo Ivo Cortez Teixeira. Introduction to Statistical Physics. Cambridge University Press, 2014. (Direct link for U of Utah access: https://doi-org.ezproxy.lib.utah.edu/10.1017/CBO9781107284180). doi:10.1017/CBO9781107284180.

[ICVG20]

Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray. Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Updated Edition. Princeton University Press, 2020. (Direct link for U of Utah access: https://www-jstor-org.ezproxy.lib.utah.edu/stable/j.ctvrxk1hs). doi:10.2307/j.ctvrxk1hs.

[Mur22]

Kevin P. Murphy. Probabilistic Machine Learning: An introduction. MIT Press, 2022. (Direct link to the 2023-06-21 draft PDF file: probml/pml-book). URL: https://probml.github.io/pml-book/book1.html.

[Owe13]

Art B. Owen. Monte Carlo theory, methods and examples. 2013. URL: https://artowen.su.domains/mc/.