000			02548 a2200253 4500
003			OSt
005			20220112092424.0
008			220105b xxu||||| |||| 00| 0 eng d
020			_a9780367139919
040			_cIIT Kanpur
041			_aeng
082			_a519.542 _bM154s2
100			_aMcElreath, Richard
245			_aStatistical rethinking [2nd ed.] _ba Bayesian course with examples in R and Stan _cRichard McElreath
250			_a2nd ed.
260			_bCRC Press _c2020 _aBoca Raton
300			_axvii, 593p
440			_aTexts in statistical science series
490			_a/ edited by Joseph K. Blitzstein ...[et al.]
520			_aStatistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work. The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding. The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses. Features Integrates working code into the main text Illustrates concepts through worked data analysis examples Emphasizes understanding assumptions and how assumptions are reflected in code Offers more detailed explanations of the mathematics in optional sections Presents examples of using the dagitty R package to analyze causal graphs Provides the rethinking R package on the author's website and on GitHub
650			_aBayesian statistical decision theory
650			_aR (Computer program language)
942			_cBK
999			_c565080 _d565080