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CASt online resources

Statistics books by astronomers and physicists
Bayesian Logical Data Analysis for the Physical Sciences
    by P. C. Gregory (2005). After introduction to probabilities, Bayesian and frequentist inference, this monograph presents Bayesian applications to data with Gaussian and Poisson errors, linear and nonlinear modelling, maximum entropy, Markov chain Monte Carlo integration, and spectral analysis of time series. Provides worked examples with astronomical data, problem sets, and codes associated with Mathematica.
Multivariate Data Analysis for Astrophysics
    by Fionn Murtagh (c. 2005). Unpublished lecture notes for a graduate course for astronomers with astronomical applications emphasizing clustering (hierarchical, minimal spanning tree, Voronoi tesselation, partitioning, mixture models, self-organizing maps, ultrametric spaces, P-adic coding), and discriminant analysis (Bayes and nonparametric discrimination, multilayer perceptron). An update of his 1987 monograph appears here.
Bootstrap Techniques for Signal Processing
    by Abdelhak M. Zoubir and D. Robert Iskander (2004). This book serves as a handbook on `bootstrap' for engineers, to analyze complicated data with little or no model assumptions. Bootstrap has found many applications in engineering field including, artificial neural networks, biomedical engineering, environmental engineering, image processing, and Radar and sonar signal processing. Majority of the applications are taken from signal processing literature.
Practical Statistics for Astronomers
    by Jasper V. Wall & C. R. Jenkins (2003).  Textbook on methods relevant for observational astronomy with emphasis on Bayesian approaches and worked problems. Covers probability, correlation, hypothesis testing, Bayesian models, time series analysis, luminosity functions and clustering.
Data Reduction and Error Analysis for the Physical Sciences
    by Philip Bevington & D. Keith Robinson (2003, 2nd ed.).  Popular text with code covering error analysis, Monte Carlo techniques, least-squares fitting, maximum-likelihood, and goodness-of-fit.
Astronomical Image and Data Analysis
    by Jean-Luc Starck and Fionn Murtagh (2002).  Advanced techniques for treating astronomical data including data filtering & storage, image processing (edge detection, segmentation, pattern recognition), image compression, source detectcion, multiscale analysis using wavelet transforms, deconvolution, multivariate data, entropies, catalogs and Virtual Observatories. 
Statistics of the Galaxy Distribution
    by Vicent J. Martinez and Enn Saar (2002).  Comprehensive monograph on the large-scale galaxy of the universe traced by galaxy redshift surveys.  It reviews the astronomical observations, statistical techniques and cosmological inferences.
Principles of Data Analysis
    by Prasenjit Saha.  Brief introductory book covering Gaussian & Poisson distributions, Monte Carlo methods, least squares, nonlinear regression, and entropy.  Available free on the Web.
Probability and Statistics in Experimental Physics
    by Byron P. Roe (2001, 2nd edition)  Textbook  covering basic comcepts, statistical distributions, Monte Carlo methods, central limit theorem, correlation coefficients, curve fitting with constraints andconfidence belts, likelihood ratios, least-squares and robust estimation (including errors in x and y), Poisson problems.  Problem sets with solutions. 
Data Analysis: Statistical and Computational Methods for Scientists and Engineers
    by Siegmund Brandt (1999, 3rd edition).   Volume bridging the gap between physical experiment and statistical theory.  Includes Monte Carlo, maximum likelihood and least squares methods; hypothesis tests, analysis of variance, polynomial regression, and time series analysis.  Code on CD-ROM. 
Statistical Data Analysis
    by Glen Cowan (1998).  Guide to practical applications of statistics in expermental physical science with examples from particle physics.  Includes Monte Carlo methods; parameter estimation (maximum likelihood, least squares, moments); errors, limits and confidence intervals; and characteristic functions.
Image processing and Data Analysis: The Multiscale Approach
    by J.-L. Starck, F. Murtagh & A. Bijaoui (1998).  Technical monograph on the use of wavelets for multiscale analysis of astronomical, engineering, remote sensing, and medical images.
    by G. Jogesh Babu & Eric D. Feigelson (1996).  A review of research topics on the methodology of astronomical data analysis including resampling methods, spatial point processes, symmetrical linear regressions, multivariate classification, time series analysis, censoring and truncation.  Introduces astronomical problems to statisticians.
Data Analysis: A Bayesian Tutorial
    by Devinder Sivia (1996).  A clear introduction by a physicist covering parameter estimation, model selection, assigning probabilities, nonparametric estimation, and experimental design. 
Statistics: A Guide to the Use of Statistical Methods in the Physical Sciences
    by Roger J. Barlow (1993).  Introductory treatment of parameter estimation (least squares, maximum likelihood), hypothesis testing, Bayesian statistics and non-parametric methods.
Statistics in Theory and Practice
    by Robert Lupton (1993).  Intermediate-level monograph aimed at physical scientists explaining probability distributions, sampling statistics, confidence intervale, hypothesis testing, maximum likelihood estimation, goodness-of-fit, and nonparametric rank tests.  Includes problems with answers.
A Practical Guide to Data Analysis for Physical Science Students
    by Louis Lyons (1991).  Undergraduate text for interpretation of experiments including distributions and moments, Gaussian errors, combining results, least-squares fitting, weighting and confidence intervals, parameter testing. 
Some older volumes of interest include:
    Statistics for nuclear and particle physicists (Louis Lyons, 1986)
    E. T. Jaynes: Papers on probability, statistics and statistical physics (E. T. Jaynes, 1989)
    Multivariate Data Analysis (Fionn Murtagh, 1987)
    The History of Statistics: The Measurement of Uncertainty Before 1900 (Stephen M. Stigler, 1986)
    Statistical Methods in Experimental Physics (W. A. Eadie et al., 1971)
    Method of least squares and principles of the theory of observations (I. V. Linnik, 1961)
    Combination of Observations (William M. Smart, 1958)
    Statistical Astronomy (Robert J. Trumpler & Harold F. Weaver, 1953)
    Introduction to Physical Statistics (Robert B. Lindsay, 1941)
    Scientific Inference (Harold Jeffreys, 1937)
    On the algebraical and numerical theory of errors of observations and the combination of observations (George B. Airy, 1861)
    Theory of the combination of observations least subject to error (Carl F. Gauss, 1823, English ed 1995)

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