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Statistical Rethinking, 2nd Edition /
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....
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| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Chapman and Hall/CRC,
2020.
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| Edición: | 2nd edition. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a McElreath, Richard, |e author. | |
| 245 | 1 | 0 | |a Statistical Rethinking, 2nd Edition / |c McElreath, Richard. |
| 250 | |a 2nd edition. | ||
| 264 | 1 | |b Chapman and Hall/CRC, |c 2020. | |
| 300 | |a 1 online resource (594 pages) | ||
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| 520 | |a 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. | ||
| 542 | |f Copyright © Chapman and Hall/CRC 2020 |g 2020 | ||
| 550 | |a Made available through: Safari, an O'Reilly Media Company. | ||
| 588 | 0 | |a Online resource; Title from title page (viewed March 13, 2020). | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 710 | 2 | |a O'Reilly for Higher Education (Firm), |e distributor. | |
| 710 | 2 | |a Safari, an O'Reilly Media Company. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9780429639142/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||