Skip to main content
Presented to you through Paradigm Publishing Services

Princeton University Press

Visit our Partner Page See all our books
Chapter
Licensed
Unlicensed Requires Authentication

A. An Introduction to Scientific Computing with Python

  • , and
© 2020 Princeton University Press, Princeton

© 2020 Princeton University Press, Princeton

Chapters in this book

  1. Frontmatter i
  2. Contents v
  3. Preface ix
  4. I. Introduction
  5. 1. About the Book and Supporting Material 1
  6. 2. Fast Computation on Massive Data Sets 41
  7. II. Statistical Frameworks and Exploratory Data Analysis
  8. 3. Probability and Statistical Distributions 65
  9. 4. Classical Statistical Inference 115
  10. 5. Bayesian Statistical Inference 165
  11. III. Data Mining and Machine Learning
  12. 6. Searching for Structure in Point Data 243
  13. 7. Dimensionality and Its Reduction 281
  14. 8. Regression and Model Fitting 311
  15. 9. Classification 353
  16. 10. Time Series Analysis 399
  17. IV. Appendices
  18. A. An Introduction to Scientific Computing with Python 467
  19. B. AstroML:Machine Learning for Astronomy 505
  20. C. Astronomical Flux Measurements and Magnitudes 509
  21. D. SQL Query for Downloading SDSS Data 513
  22. E. Approximating the Fourier Transform with the FFT 515
  23. Visual Figure Index 521
  24. Index 529
Statistics, Data Mining, and Machine Learning in Astronomy
This chapter is in the book Statistics, Data Mining, and Machine Learning in Astronomy
https://doi.org/10.1515/9780691197050-012

Chapters in this book

  1. Frontmatter i
  2. Contents v
  3. Preface ix
  4. I. Introduction
  5. 1. About the Book and Supporting Material 1
  6. 2. Fast Computation on Massive Data Sets 41
  7. II. Statistical Frameworks and Exploratory Data Analysis
  8. 3. Probability and Statistical Distributions 65
  9. 4. Classical Statistical Inference 115
  10. 5. Bayesian Statistical Inference 165
  11. III. Data Mining and Machine Learning
  12. 6. Searching for Structure in Point Data 243
  13. 7. Dimensionality and Its Reduction 281
  14. 8. Regression and Model Fitting 311
  15. 9. Classification 353
  16. 10. Time Series Analysis 399
  17. IV. Appendices
  18. A. An Introduction to Scientific Computing with Python 467
  19. B. AstroML:Machine Learning for Astronomy 505
  20. C. Astronomical Flux Measurements and Magnitudes 509
  21. D. SQL Query for Downloading SDSS Data 513
  22. E. Approximating the Fourier Transform with the FFT 515
  23. Visual Figure Index 521
  24. Index 529
Downloaded on 26.4.2026 from https://www.degruyterbrill.com/document/doi/10.1515/9780691197050-012/html?lang=en
Scroll to top button