Item #:
103564563
Bayesian Analysis with Python: A practical guide to probabilistic modeling
87% of respondents would recommend this to a friend
Item #:
103564563
CZK 2149
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You will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges.
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What Stands Out
Practical Insights
Offers hands-on approaches to Bayesian analysis, providing practical techniques that enable readers to apply probabilistic modeling in real-world scenarios effectively.
Comprehensive Coverage
Covers essential algorithms and models in-depth, ensuring readers gain a thorough understanding of both foundational and advanced Bayesian methods through clear explanations.
User-Friendly Format
Designed with clarity and accessibility in mind, making complex concepts approachable for learners and practitioners, enhancing their ability to implement Bayesian techniques confidently.
Product Details
| Publisher | Packt Publishing |
| Publication date | 31 Jan. 2024 |
| Edition | 3rd |
| Language | English |
| Print length | 394 pages |
| ISBN-10 | 1805127160 |
| ISBN-13 | 978-1805127161 |
| Item weight | 676 g |
| Dimensions | 19.05 x 2.26 x 23.5 cm |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to leverage Bayesian methods for predictive analytics and decision-making.
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Statistical Analysts
Statistical analysts seeking a practical approach to probabilistic modeling will find this guide valuable and informative.
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Graduate Students
Graduate students in statistics or data science needing a comprehensive resource on Bayesian analysis techniques.
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Beginners
Not suitable for absolute beginners in statistics or programming due to the complexity of Bayesian concepts.
Product Description
Bayesian Analysis with Python: A practical guide to probabilistic modeling
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Editorial Review
**** Osvaldo Martin’s "Bayesian Analysis with Python" is a commendable resource for anyone venturing into the realm of Bayesian inference using Python. This book uniquely caters to readers who have a foundational understanding of Python but may not be well-versed in statistics or Bayesian techniques, making it approachable for beginners while still providing substantive content for more seasoned practitioners. The practical emphasis of the book is one of its most significant strengths. Each chapter concludes with exercises that help solidify the concepts learned, and the inclusion of a dedicated Discord community fosters an environment for continued discussion and collaborative learning. The introductory chapters offer an insightful blend of theoretical frameworks and practical implementation, which is particularly beneficial for newcomers to the subject. The seamless integration of PyMC, a leading probabilistic programming language, allows readers to apply their learning in meaningful ways. Subsequent chapters delve into a variety of specific modeling strategies, such as hierarchical models and Bayesian adaptive regression trees (BART), all delivered with clarity and ample examples. The author provides insights on critical topics including model comparison and evaluation, which are indispensable for data scientists. Those looking for practical applications will find the chapter on Bambi particularly illuminating, showcasing efficient methods for Constructing models with a focus on visual clarity. Moreover, the book navigates the nuances of inference engines and offers a thorough reference for understanding Bayesian sampling methods, making it a valuable text for both educators and practitioners. Overall, "Bayesian Analysis with Python" not only bridges foundational concepts with practical applications but also enriches the reader with essential resources for further exploration. With a wealth of hands-on examples and a comprehensive approach, this book is an invaluable asset for anyone aiming to develop expertise in Bayesian analysis. **
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Pros
- Accessible for beginners with basic Python knowledge
- Strong practical approach with exercises at the end of chapters
- Inclusion of a supportive online community for discussion
- Comprehensive coverage of theoretical and computational aspects
- Insightful chapters on specific modeling strategies
- Clear explanations of complex concepts
- Valuable resources for further learning provided
Cons
- Some readers may benefit from more real-time, hands-on examples
Product Price History
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CZK 2149
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Features & Benefits
- Learn Bayesian modeling with state-of-the-art Python libraries.
- Step-by-step guidance for conducting Bayesian data analysis.
- Enhanced learning with sample problems and practice exercises.
- Includes free PDF eBook with purchase of print or Kindle version.
- Explore various models, including hierarchical and generalized linear models.
- No prior statistical knowledge required; ideal for beginners and professionals.
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