Show Summary Details
- Title Pages
- Preface
- 1 About the Book and Supporting Material
- 2 Fast Computation on Massive Data Sets
- 3 Probability and Statistical Distributions
- 4 Classical Statistical Inference
- 5 Bayesian Statistical Inference
- 6 Searching for Structure in Point Data
- 7 Dimensionality and Its Reduction
- 8 Regression and Model Fitting
- 9 Classification
- 10 Time Series Analysis
- A. An Introduction to Scientific Computing with Python
- B. AstroML: Machine Learning for Astronomy
- C. Astronomical Flux Measurements and Magnitudes
- D. SQL Query for Downloading SDSS Data
- E. Approximating the Fourier Transform with the FFT
- References
- Visual Figure Index
- Index
(p.515) C. Astronomical Flux Measurements and Magnitudes
(p.515) C. Astronomical Flux Measurements and Magnitudes
- Source:
- Statistics, Data Mining, and Machine Learning in Astronomy
- Author(s):
Željko Ivezić
Andrew J. Connolly
Jacob T. VanderPlas
Alexander Gray
- Publisher:
- Princeton University Press
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- Title Pages
- Preface
- 1 About the Book and Supporting Material
- 2 Fast Computation on Massive Data Sets
- 3 Probability and Statistical Distributions
- 4 Classical Statistical Inference
- 5 Bayesian Statistical Inference
- 6 Searching for Structure in Point Data
- 7 Dimensionality and Its Reduction
- 8 Regression and Model Fitting
- 9 Classification
- 10 Time Series Analysis
- A. An Introduction to Scientific Computing with Python
- B. AstroML: Machine Learning for Astronomy
- C. Astronomical Flux Measurements and Magnitudes
- D. SQL Query for Downloading SDSS Data
- E. Approximating the Fourier Transform with the FFT
- References
- Visual Figure Index
- Index
