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008 140125t20142014njua ob 001 0 eng d
020 9781400848911|q(electronic bk.)
020 1400848911|q(electronic bk.)
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049 MAIN
050 4 QA76.9.D343 .I384 2014
050 4 QB149|b.I949 2014
082 04 006.312
100 1 Ivezić, Željko.
245 10 Statistics, data mining, and machine learning in astronomy
:|ba practical Python guide for the analysis of survey
data /|cŽeljko Ivezić, Andrew J. Connolly, Jacob T.
VanderPlas, and Alexander Gray.
264 1 Princeton, N.J. :|bPrinceton University Press,|c2014.
264 4 |c©2014
300 1 online resource (x, 540 pages) :|billustrations.
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
490 1 Princeton series in modern observational astronomy
505 0 I. Introduction -- 1. About the Book and Supporting
Material -- 1.1. What do Data Mining, Machine Learning,
and Knowledge Discovery mean? -- 1.2. What is this book
about? -- 1.3. An incomplete survey of the relevant
literature -- 1.4. Introduction to the Python Language and
the Git Code Management Tool -- 1.5. Description of
surveys and data sets used in examples -- 1.6. Plotting
and visualizing the data in this book -- 1.7. How to
efficiently use this book -- References -- 2. Fast
Computation on Massive Data Sets -- 2.1. Data types and
Data Management systems -- 2.2. Analysis of algorithmic
efficiency -- 2.3. Seven types of computational Problem[s]
-- 2.4. Seven strategies for speeding things up -- 2.5.
Case studies: Speedup strategies in practice --
References.
505 8 II. Statistical Frameworks and Exploratory Data Analysis -
- 3. Probability and Statistical Distributions -- 3.1.
Brief overview of probability and random variables -- 3.2.
Descriptive statistics -- 3.3. Common Univariate
Distribution Functions -- 3.4. The Central Limit Theorem -
- 3.5. Bivariate and Multivariate Distribution Functions -
- 3.6. Correlation coefficients -- 3.7. Random number
generation for arbitrary distributions -- References -- 4.
Classical Statistical Inference -- 4.1. Classical vs.
Bayesian Statistical Inference -- 4.2. Maximum Likelihood
Estimation (MLE) -- 4.3. The goodness of Fit and Model
Selection -- 4.4. ML Applied to Gaussian Mixtures: The
Expectation Maximization Algorithm -- 4.5. Confidence
estimates: the bootstrap and the jackknife -- 4.6.
Hypothesis testing -- 4.7. Comparison of distributions --
4.8. Nonparametric modeling and histograms -- 4.9.
Selection effects and Luminosity Function Estimation --
4.10. Summary -- References -- 5 Bayesian Statistical
Inference -- 5.1. Introduction to the Bayesian method --
5.2. Bayesian priors -- 5.3. Bayesian parameter
uncertainty quantification -- 5.4. Bayesian model
selection -- 5.5. Nonuniform priors: Eddington, Malmquist,
and Lutz-Kelker biases -- 5.6. Simple examples of Bayesian
analysis: Parameter estimation -- 5.7. Simple examples of
Bayesian analysis: Model selection -- 5.8. Numerical
methods for complex problems (MCMC) -- 5.9. Summary of
pros and cons for classical and Bayesian methods --
References.
505 8 III. Data Mining and Machine Learning -- 6 Searching for
Structure in Point Data -- 6.1. Nonparametric density
estimation -- 6.2. Nearest-neighbor density estimation --
6.3. Parametric density estimation -- 6.4. Finding
clusters in data -- 6.5. Correlation functions -- 6.6.
Which density estimation and clustering algorithms should
I use? -- References -- 7 Dimensionality and its reduction
-- 7.1. The curse of dimensionality -- 7.2. The data sets
used in this chapter -- 7.3. Principal component analysis
-- 7.4. Nonnegative matrix factorization -- 7.5. Manifold
learning -- 7.6. Independent component analysis and
projection pursuit -- 7.7. Which dimensionality reduction
technique should I use? -- References -- 8 Regression and
model fitting -- 8.1. Formulation of the regression
problem -- 8.2. Regression for linear models -- 8.3.
Regularization and penalizing the likelihood -- 8.4.
Principal component regression -- 8.5. Kernel regression -
- 8.6. Locally linear regression -- 8.7. Nonlinear
regression -- 8.8. Uncertainties in the data -- 8.9.
Regression that is robust to outliers -- 8.10. Gaussian
process regression -- 8.11. Overfitting, underfitting, and
cross-validation -- 8.12. Which regression method should I
use? -- References.
505 8 III. Data Mining and Machine Learning (continued) -- 9
Classification -- 9.1. Data sets used in this chapter --
9.2. Assigning categories: Classification -- 9.3.
Generative classification -- 9.4. K-nearest-neighbor
classifier -- 9.5. Discriminative classification -- 9.6.
Support vector machines -- 9.7. Decision trees -- 9.8.
Evaluating classifiers: ROC Curves -- 9.9. Which
classifier should I use? -- References -- 10 Time Series
Analysis -- 10.1. Main concepts for Time Series Analysis -
- 10.2. Modeling toolkit for Time Series Analysis -- 10.3.
Analysis of Periodic Time Series -- 10.4. Temporally
localized signals -- 10.5. Analysis of Stochastic
Processes -- 10.6. Which method should I use for Time
Series Analysis? -- References.
505 8 IV. Appendices -- A An Introduction to Scientific
Computing with Python -- A.1. A brief history of Python --
A.2. The ScyPy universe -- A.3. Getting started with
Python -- A.4. IPython: The basics of interactive
computing -- A.5. Introduction to NumPy -- A.6.
Visualization with Matplotlib -- A.7. Overview of useful
NumPy/SciPy modules -- A.8. Efficient coding with Python
and NumPy -- A.9. Wrapping existing code in Python --
A.10. Other resources -- B AstroML: Machine Learning for
Astronomy -- B.1. Introduction -- B.2. Dependencies --
B.3. Tools included in AstroML v0.1 -- C Astronomical Flux
Measurements and Magnitudes -- C.1. The definition of the
specific flux -- C.2. Wavelength window function for
astronomical measurements -- C.3. The astronomical
magnitude systems -- D SQL Query for Downloading SDSS Data
-- E Approximating the Fourier Transform with the FFT --
References.
506 1 Unlimited number of concurrent users.|5UkHlHU
650 0 Statistical astronomy.
650 0 Astronomy|xData processing.
650 0 Python (Computer program language)
700 1 Connolly, Andrew|q(Andrew J.)
700 1 Vanderplas, Jacob T.
700 1 Gray, Alexander|q(Alexander G.)
830 0 Princeton series in modern observational astronomy.
856 40 |uhttps://www.jstor.org/stable/10.2307/j.ctt4cgbdj|zGo to
ebook
936 JSTOR-D-2016/17
