Leseprobe
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Statistics, Data Mining, and Machine Learning in Astronomy
Željko Ivezić, Andrew J. Connolly, Alexander Gray, et al.
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Naturwissenschaften, Medizin, Informatik, Technik / Naturwissenschaften allgemein
Beschreibung
Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest.
An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.
- Fully revised and expanded
- Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets
- Features real-world data sets from astronomical surveys
- Uses a freely available Python codebase throughout
- Ideal for graduate students, advanced undergraduates, and working astronomers
Kundenbewertungen
Accuracy and precision, Normal distribution, Boosting (machine learning), Sloan Digital Sky Survey, Variance, Sobolev space, Likelihood function, Online machine learning, Computation, Fundamental plane (elliptical galaxies), Hyperparameter, Astronomy, Proportionality (mathematics), Spline (mathematics), Density estimation, Mathematical optimization, Statistical hypothesis testing, Monte Carlo method, Bayesian, Expectation value (quantum mechanics), Estimation, Estimation theory, Measurement, Orbital resonance, Expectation–maximization algorithm, Astronomical survey, Dimension, Two-dimensional space, Curve fitting, Variational method (quantum mechanics), Convolution theorem, Scientific notation, Mixture model, Machine learning, Statistic, Luminosity function (astronomy), Asymptotic theory (statistics), Eigenvalues and eigenvectors, Convex optimization, Observational astronomy, Cross-validation (statistics), Space Telescope Science Institute, Markov chain Monte Carlo, Regularization (mathematics), Algorithm, Central limit theorem, Tests of general relativity, Variable (mathematics), Probability distribution, Statistical inference, Data set, Linear regression, Sample space, Bayesian statistics, Fourier transform, Result, Vectorization (mathematics), Principal component analysis, Probability, Histogram, Kernel density estimation, Time series, Autoencoder, Cluster analysis, Bayesian inference, Notation in probability and statistics, Observational error, Pseudorandom number generator, Parameter (computer programming), Estimator