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
