03887cam a2200601Ki 4500 000717159 FlBoTFG 20240803130220.0 20200330012813.0 m o d cr cnu unuuu 200318t20202020flu o 000 0 eng d NEZPRACOVANÝ IMPORT 9780429029608 electronic book 0429029608 electronic book 9780429635977 electronic book Mobipocket 0429635974 electronic book Mobipocket 9780429639142 electronic book EPUB 0429639147 electronic book EPUB 9780429642319 electronic book electronic book 0429642318 electronic book electronic book 9780367139919 036713991X (OCoLC)1145123627 (OCoLC-P)1145123627 QA279.5 .M3975 2020 MAT 029000 bisacsh UFM bicssc 519.542 23 McElreath, Richard, 1973- author. Statistical rethinking : a Bayesian course with examples in R and Stan / Richard McElreath. Second edition. Boca Raton : Chapman & Hall/CRC, 2020. ©2020 1 online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier Texts in statistical science Previous edition: Boca Raton: CRC Press/Taylor & Francis Group, 2016. Statistical 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 OCLC-licensed vendor bibliographic record. Bayesian statistical decision theory. R (Computer program language) MATHEMATICS / Probability & Statistics / General bisacsh Taylor & Francis https://zdroje.vse.cz/login?url=https://www.taylorfrancis.com/books/9780429029608 Plný text TF EB PhDinfra PAR 000680685 UEP01 776 E-book Tištěná verze NEPOSILAT EB