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This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. A must-have for astronomers, its concrete focus on modeling, analysis, and interpretation will also be attractive to researchers in the sciences more broadly.
Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics.
Reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. This book advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals.
"Preface This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied...
Surveys the classical economic theory of Bayesian mechanism design and recent advances from the perspective of algorithms and approximation. Classical economics gives simple characterizations of Bayes-Nash equilibrium and optimal mechanisms when the agents' preferences are linear and single-dimensional.
Offers an account of Bayes' rule for general readers. This title 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.