References and tutorials



References and tutorials

Here are a few references and tutorials we think might be useful.  They are arranged by topic in alphabetical order, with a short description indicating the level of difficulty. Many topics overlap, so for example, you might find useful information on Bayesian statistics in an image processing resource.  Follow the links to the resources themselves, or at least to where you might obtain them.  This is not intended to be an exhaustive list.  There are many books out there on all these topics.  If you have any suggestions for topics, or resources, please contact us!


  • Image processing
    1. Digital Image Processing (R. C. Gonzalez & R. E. Woods)
      • fundamental and basic algorithms in image processing.
  • Solar Physics
  • Sparse image processing
  • Statistics and treatment of errors
    1. Modern Statistical Methods for Astronomers with R Applications (E. D. Feigelson & G. J. Babu)
      • fundamental results of probability theory and statistical inference, before exploring several fields of applied statistics, such as data smoothing, regression, multivariate analysis and classification, treatment of non-detections, time series analysis, and spatial point processes (advanced undergraduate).
    2. An Introduction to the Bootstrap (B. Efron & R. J. Tibshirani)
      • one of the first reference books that describes the bootstrap and other methods for assessing statistical accuracy. The first six chapters give the basics of the bootstrap; the following chapters delve into particular issues (graduate level).
  • Time series analysis



There are plenty of tutorials out there.  YouTube contains a large number of tutorial videos that can be a great way to learn.  Here are some recommendations.

  • mathematicalmonk
    • Videos about mathematics, at the graduate level or upper-level undergraduate.  Includes probability, optimization and machine learning.

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