# Recommended Reading

There are lots of great resources beyond this website. Here are some especially good ones.

## Programming

- Nazarathy & Klok (2021) offers tutorials on using Julia for statistics and machine learning; reading good code is a great way to improve your coding
- Lauwens & Downey (2019) teaches key concepts in programming, using Julia for all examples. It’s a great resource for beginners trying to understand code and for more experienced programmers who want to write better code.

## Fundamentals

- Blitzstein & Hwang (2019) provides a thorough introduction to key concepts and ideas in probability. The book accompanies a free online course, Stat 110, which is a great resource for learning probability and statistics. Practice problems and solutions, handouts, and lecture videos are all available online.
- Downey (2021) offers an introduction to Bayesian statistics using computational methods. It’s not environment focused but provides code and a clear explanation of core concepts.

## Digging deeper

- Gelman et al. (2014) is a classical and detailed textbook on Bayesian inference.
- Gelman (2021) is a less technical textbook with clear and well-worked examples (mostly not environmental).
- Friedman et al. (2001) is a classic introduction to machine learning, which complements the Bayesian perspective nicely
- Cressie & Wikle (2011) provides a detailed exploration of hierarchical space-time models. There have been some computational advances since then that are worth keeping in mind before you apply these models directly, but it’s a clearly written and overview.
- The documentation for the Turing, PyMC, and (especially) stan probabilistic programming languages offer outstanding tutorials on statistical modeling

## Environmental applications

- Helsel et al. (2020) is a textbook also produced by the USGS covering some general statistical methods with a water resources focus. This book is very focused on hydrological statistics, hypothesis testing, and frequency estimates, but there are still some valuable insights.

## References

Blitzstein, J. K., & Hwang, J. (2019).

*Introduction to Probability, Second Edition*(2nd Edition). Boca Raton: Chapman and Hall/CRC. Retrieved from http://probabilitybook.net
Cressie, N. A. C., & Wikle, C. K. (2011).

*Statistics for spatio-temporal data*. Hoboken, N.J.: Wiley.
Downey, A. B. (2021).

*Think Bayes*. "O’Reilly Media, Inc.". Retrieved from https://allendowney.github.io/ThinkBayes2/
Friedman, J., Hastie, T., & Tibshirani, R. (2001).

*The Elements of Statistical Learning*(Vol. 1). Springer series in statistics Springer, Berlin.
Gelman, A. (2021).

*Regression and other stories*. Cambridge, United Kingdom ; Cambridge University Press.
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2014).

*Bayesian Data Analysis*(3rd ed.). Chapman & Hall/CRC Boca Raton, FL, USA.
Helsel, D. R., Hirsch, R. M., Ryberg, K. R., Archfield, S. A., & Gilroy, E. J. (2020).

*Statistical methods in water resources*.*Techniques and Methods*. U.S. Geological Survey. https://doi.org/10.3133/tm4A3
Lauwens, B., & Downey, A. B. (2019).

*Think Julia: How to think like a computer scientist*. O’Reilly Media.
Nazarathy, Y., & Klok, H. (2021).

*Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence*. Springer International Publishing. https://doi.org/10.1007/978-3-030-70901-3