BayesBasics.bib

@book{kruschke_doing_2010,
	title = {Doing Bayesian Data Analysis: A Tutorial Introduction with R},
	isbn = {9780123814869},
	shorttitle = {Doing Bayesian Data Analysis},
	abstract = {There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and {BUGS}, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and ‘rusty’ calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and {BUGS} software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research {needs.The} textbook bridges the students from their undergraduate training into modern Bayesian methods.-Accessible, including the basics of essential concepts of probability and random sampling -Examples with R programming language and {BUGS} software -Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance ({ANOVA)} and comparisons in {ANOVA}, multiple regression, and chi-square (contingency table analysis). -Coverage of experiment planning -R and {BUGS} computer programming code on website -Exercises have explicit purposes and guidelines for accomplishment},
	language = {en},
	publisher = {Academic Press},
	author = {Kruschke, John},
	month = nov,
	year = {2010},
	keywords = {Mathematics / Applied, Mathematics / General}
}

@book{kruschke2014doing,
  title={Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan},
  author={Kruschke, John},
  year={2014},
  publisher={Academic Press}
}

@book{gelman_bda,
	edition = {3rd},
	title = {Bayesian Data Analysis},
	isbn = {9781439840955},
	abstract = {Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition   Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code   The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.},
	language = {en},
	url = {http://www.stat.columbia.edu/~gelman/book/},
	author = {Gelman, Andrew and Carlin, John B. and Stern, Hal S. and Dunson, David B. and Vehtari, Aki and Rubin, Donald B.},
	month = nov,
	year = {2013},
	keywords = {Computers / Mathematical \& Statistical Software, Mathematics / Probability \& Statistics / General, Psychology / Research \& Methodology}
}

@book{gelman_arm,
	title = {Data Analysis Using Regression and {Multilevel/Hierarchical} Models},
	isbn = {9781139460934},
	abstract = {Data Analysis Using Regression and {Multilevel/Hierarchical} Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.},
	language = {en},
	publisher = {Cambridge University Press},
	author = {Gelman, Andrew and Hill, Jennifer},
	month = dec,
	year = {2006},
	keywords = {Mathematics / Probability \& Statistics / General, Political Science / General, Psychology / Assessment, Testing \& Measurement, Social Science / Research}
}

@book{bugsbook,
	address = {Boca Raton, {FL}},
	title = {The {BUGS} Book: A Practical Introduction to Bayesian Analysis},
	isbn = {9781584888499},
	shorttitle = {The {BUGS} Book},
	abstract = {Bayesian statistical methods have become widely used for data analysis and modelling in recent years, and the {BUGS} software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software, The {BUGS} Book provides a practical introduction to this program and its use. The text presents complete coverage of all the functionalities of {BUGS}, including prediction, missing data, model criticism, and prior sensitivity. It also features a large number of worked examples and a wide range of applications from various disciplines.  The book introduces regression models, techniques for criticism and comparison, and a wide range of modelling issues before going into the vital area of hierarchical models, one of the most common applications of Bayesian methods. It deals with essentials of modelling without getting bogged down in complexity. The book emphasises model criticism, model comparison, sensitivity analysis to alternative priors, and thoughtful choice of prior distributions—all those aspects of the "art" of modelling that are easily overlooked in more theoretical expositions.   More pragmatic than ideological, the authors systematically work through the large range of "tricks" that reveal the real power of the {BUGS} software, for example, dealing with missing data, censoring, grouped data, prediction, ranking, parameter constraints, and so on. Many of the examples are biostatistical, but they do not require domain knowledge and are generalisable to a wide range of other application areas.   Full code and data for examples, exercises, and some solutions can be found on the book’s website.},
	language = {English},
	publisher = {Chapman and {Hall/CRC}},
	author = {Lunn, David and Jackson, Chris and Best, Nicky and Thomas, Andrew and Spiegelhalter, David},
	month = oct,
	year = {2012}
}

@book{jeff_gill_bayesian_2008,
	address = {Boca Raton},
	edition = {Second},
	title = {Bayesian methods : a social and behavioral sciences approach},
	isbn = {9781584885627},
	lccn = {QA 279.5},
	shorttitle = {Bayesian methods},
	language = {eng},
	publisher = {Chapman \& {Hall/CRC}},
	author = {Gill, Jeff},
	year = {2008},
	keywords = {Bayes' solution, Bayesian analysis, Bayesian statistical decision theory., Decisión estadística., Methode van Bayes., Social sciences Statistical methods., Teorías bayesian.}
}

@book{simon_jackman_bayesian_2009,
	address = {Chichester, {UK}},
	title = {Bayesian analysis for the social sciences},
	isbn = {9780470011546},
	lccn = {HA 29},
	language = {eng},
	publisher = {Wiley},
	author = {Jackman, Simon},
	year = {2009},
	keywords = {Bayes' solution, Bayesian analysis, Bayesian statistical decision theory., Bayes-Verfahren., Social sciences Statistical methods.}
}

@book{scott_lynch_2007,
	address = {New York},
	title = {Introduction to applied Bayesian statistics and estimation for social scientists},
	isbn = {9780387712642},
	abstract = {Lynch covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of the book is that it covers models that are most commonly used on social science research.},
	language = {eng},
	publisher = {Springer},
	author = {Lynch, Scott M.},
	collaborator = {ebrary, Inc},
	year = {2007},
	keywords = {Bayes' solution, Bayesian analysis, Bayesian statistical decision theory., Social sciences Statistical methods.}
}

@book{mcgrayne_theory_2012,
	address = {New Haven Conn.},
	edition = {Reprint},
	title = {The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy},
	isbn = {9780300188226},
	shorttitle = {The Theory That Would Not Die},
	abstract = {Drawing on primary source material and interviews with statisticians and other scientists, {"The} Theory That Would Not Die" is the riveting account of how a seemingly simple theorem ignited one of the greatest scientific controversies of all time. Bayes' rule appears to be a straightforward, one-line theorem: by updating our initial beliefs with objective new information, we get a new and improved belief. To its adherents, it is an elegant statement about learning from experience. To its opponents, it is subjectivity run amok. In the first-ever account of Bayes' rule for general readers, Sharon Bertsch {McGrayne} explores this controversial theorem and the human obsessions surrounding it. She traces its discovery by an amateur mathematician in the 1740s through its development into roughly its modern form by French scientist Pierre Simon Laplace. She reveals why respected statisticians rendered it professionally taboo for 150 years - at the same time that practitioners relied on it to solve crises involving great uncertainty and scanty information, even breaking Germany's Enigma code during World War {II}, and explains how the advent of off-the-shelf computer technology in the 1980s proved to be a game-changer. Today, Bayes' rule is used everywhere from {DNA} decoding to Homeland Security. {"The} Theory That Would Not Die" is a vivid account of the generations-long dispute over one of the greatest breakthroughs in the history of applied mathematics and statistics.},
	language = {English},
	publisher = {Yale University Press},
	author = {{McGrayne}, Sharon Bertsch},
	month = sep,
	year = {2012}
}


@book{albert_bayesian_2009,
	address = {New York},
	edition = {Second},
	title = {Bayesian Computation with R},
	isbn = {9780387922973},
	abstract = {There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the {SIR} algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo ({MCMC)} methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with {WinBUGS}, a popular {MCMC} computing language, is described with several illustrative examples. This book is a suitable companion book for an introductory course on Bayesian methods and is valuable to the statistical practitioner who wishes to learn more about the R language and Bayesian methodology. The {LearnBayes} package, written by the author and available from the {CRAN} website, contains all of the R functions described in the book. The second edition contains several new topics such as the use of mixtures of conjugate priors and the use of Zellner’s g priors to choose between models in linear regression. There are more illustrations of the construction of informative prior distributions, such as the use of conditional means priors and multivariate normal priors in binary regressions. The new edition contains changes in the R code illustrations according to the latest edition of the {LearnBayes} package.},
	language = {English},
	publisher = {Springer},
	author = {Albert, Jim},
	month = jun,
	year = {2009}
}

@article{gelmanPardoe2006,
  title={Bayesian measures of explained variance and pooling in multilevel (hierarchical) models},
  author={Gelman, Andrew and Pardoe, Iain},
  journal={Technometrics},
  volume={48},
  number={2},
  pages={241--251},
  year={2006},
  publisher={Taylor \& Francis}
}

@article{gelmanHwangVehtari,
  title={Understanding predictive information criteria for Bayesian models},
  author={Gelman, Andrew and Hwang, Jessica and Vehtari, Aki},
  journal={Statistics and Computing},
  volume={24},
  number={6},
  pages={997--1016},
  year={2014},
  publisher={Springer}
}



@book{mcelreath2016,
  title={Statistical Rethinking: A Bayesian Course with Examples in R and Stan},
  author={McElreath, Richard},
  volume={122},
  year={2016},
  publisher={CRC Press}
}

@book{mcelreath2020,
  title={Statistical rethinking: A Bayesian course with examples in R and Stan},
  author={McElreath, Richard},
  year={2020},
  publisher={Chapman and Hall/CRC}
}

@book{mcelreath2020statistical,
  title={Statistical rethinking: A Bayesian course with examples in R and Stan},
  author={McElreath, Richard},
  year={2020},
  publisher={Chapman and Hall/CRC}
}


@article{gelmanVehtariWAIC,
  title={WAIC and cross-validation in Stan},
  author={Vehtari, Aki and Gelman, Andrew},
  year={2014}
}

@article{vehtari2017practical,
  title={Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC},
  author={Vehtari, Aki and Gelman, Andrew and Gabry, Jonah},
  journal={Statistics and computing},
  volume={27},
  number={5},
  pages={1413--1432},
  year={2017},
  publisher={Springer}
}

@article{vehtari2015pareto,
  title={Pareto smoothed importance sampling},
  author={Vehtari, Aki and Simpson, Daniel and Gelman, Andrew and Yao, Yuling and Gabry, Jonah},
  journal={arXiv preprint arXiv:1507.02646},
  year={2015}
}

@article{carpenter2017stan,
  title={Stan: A probabilistic programming language},
  author={Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew D and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus and Guo, Jiqiang and Li, Peter and Riddell, Allen},
  journal={Journal of statistical software},
  volume={76},
  number={1},
  year={2017},
  publisher={Columbia Univ., New York, NY (United States); Harvard Univ., Cambridge, MA~…}
}