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Artigo n.º:
60059632
Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science)
88% of respondents would recommend this to a friend
Artigo n.º:
60059632
STD 3293340
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Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data.
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O que se Destaca
Bayesian Focus
This book emphasizes Bayesian methods, providing readers with a modern statistical framework to tackle data analysis through a probabilistic lens, enhancing their problem-solving capabilities in real-world scenarios.
Hands-On Examples
It includes practical examples using R and STAN, allowing readers to apply theoretical concepts directly. This hands-on approach facilitates deeper understanding and engagement with statistical modeling techniques.
Accessible Learning
Written in an easy-to-understand style, it caters to a wide audience, from beginners to advanced statisticians, ensuring that complex concepts are accessible to all, promoting widespread learning and application.
Detalhes do produto
- Comprehensive 2nd edition of Statistical Rethinking textbook
- Focuses on Bayesian statistics
- Includes practical examples in R and STAN
- Part of Chapman & Hall/CRC Texts in Statistical Science series
- Suitable for students and practitioners in statistics
- Offers in-depth understanding of Bayesian concepts
| Publisher | Chapman and Hall/CRC |
| Publication date | 16 Mar. 2020 |
| Edition | 2nd |
| Language | English |
| Print length | 594 pages |
| ISBN-10 | 036713991X |
| ISBN-13 | 978-0367139919 |
| Item weight | 1.43 kg |
| Dimensions | 18 x 3.5 x 26 cm |
| Part of series | Chapman & Hall/CRC Texts in Statistical Science |
Quem Deverá Comprar?
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Graduate Students
Ideal for graduate students studying statistics or related fields, providing hands-on Bayesian modeling experience.
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Data Scientists
Provides practical Bayesian techniques and examples, perfect for data scientists seeking advanced modeling skills.
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Researchers
Beneficial for researchers requiring a deeper understanding of Bayesian statistics for their analytical projects.
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Beginner Statisticians
Not suitable for beginners as it assumes prior knowledge of statistical concepts and mathematical foundations.
DESCRIÇÃO DO PRODUTO
About This Item
Looking to dive into Bayesian statistical analysis? Look no further than the "Statistical Rethinking: A Bayesian Course with Examples in R and STAN". This comprehensive textbook, now in its 2nd edition, belongs to the esteemed Chapman & Hall/CRC Texts in Statistical Science series. Whether you're a student, researcher, or data enthusiast, this book will equip you with the necessary tools and knowledge to understand and apply Bayesian methods in data analysis effectively. The author, Richard McElreath, brings his expertise in Bayesian statistical modeling to guide you through the concepts with clarity and precision. One of the standout features of this book is the practical approach it takes to teaching Bayesian statistics.
Each chapter is accompanied by examples in R and STAN, two widely-used statistical programming languages. By providing code and real-world case studies, McElreath ensures that readers not only grasp the theory behind Bayesian statistics but also gain hands-on experience in applying these methods. This edition covers a wide range of topics, including statistical decision theory, advanced statistical modeling, time series analysis and forecasting, multivariate statistical analysis, spatial statistics, and causal inference. Additionally, it delves into related areas such as statistical genetics and genomics, environmental statistics and modeling, financial and economic statistics, healthcare analytics, marketing research, and sports analytics. Whether you're a beginner or already have some experience with Bayesian statistics, this book is designed to meet you at your level.
It starts with an introduction to Bayesian statistics, making it accessible to those with no prior knowledge of this approach. As you progress through the chapters, the content becomes more advanced, catering to those looking to deepen their understanding and strengthen their statistical modeling skills. The "Statistical Rethinking: A Bayesian Course with Examples in R and STAN" is not just a textbook; it's a companion for your statistical journey. With its comprehensive coverage, practical examples, and clear explanations, this book will empower you to tackle complex data analysis problems confidently.
So, grab your copy now and unlock the power of Bayesian statistics in your research and decision-making processes.
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Epidemiology & Medical Statistics Editorial Review
**Editorial Review of "Statistical Rethinking: A Bayesian Course with Examples in R and STAN"** "Statistical Rethinking" by Richard McElreath has garnered a commendable reception among those venturing into the realm of Bayesian statistics, particularly through its hands-on approach using R and STAN. Students appreciate the book's accessibility for non-mathematicians while still maintaining rigorous academic standards, making it a suitable choice for learners at various levels. The structure of the book mimics that of a course, effectively guiding readers through complex concepts such as Markov Chain Monte Carlo (MCMC) algorithms. The introductory sections, particularly in pivotal chapters, have been highlighted as providing essential philosophical insight, which aids in cultivating a comprehensive understanding of the subject. What stands out is the book's emphasis on practical code examples, which have been essential for readers learning R in a Bayesian context. Many reviewers have praised the supplementary lecture series available on YouTube, asserting that it complements the text well, creating a robust learning experience. However, this textbook has received criticism for its length and perceived convoluted writing style. Some users felt overwhelmed by the author's penchant for storytelling and abstract explanations, suggesting that significant sections could be condensed without sacrificing educational value. This narrative style reportedly obfuscates clearer academic discussions necessary for beginners, resulting in frustration when vital concepts are buried in lengthy discussions. Overall, while "Statistical Rethinking" serves as a foundational resource for those interested in Bayesian statistics and offers significant practical guidance, potential readers should be prepared for a commitment to fully understand its content. Many find the book most beneficial when combined with the accompanying video lectures and exercises, as these formats can provide a more streamlined and engaging learning experience. **
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Prós
- Accessible to non-mathematicians while being academically rigorous.
- Effective course structure that facilitates understanding of complex concepts.
- Provides practical R code examples for hands-on learning.
- Supplemental YouTube lecture series adds value to the text.
Contras
- Lengthy and potentially convoluted writing style may confuse beginners.
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Recursos e benefícios
- Unique computational approach ensures understanding of details for reasonable choices and interpretations in modeling work
- Data analysis examples used to illustrate concepts
- Causal inference and generalized linear multilevel models presented from a simple Bayesian perspective
- New edition covers prior distributions, splines, social relations models, and more
- Includes working code and examples of using dagitty R package for causal analysis
- Rethinking R package available on author's website and on GitHub
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