Artículo nº.:
142522078
Bayesian Analysis with Python: A practical guide to probabilistic modeling
85% de los encuestados recomendarían esto a un amigo
Artículo nº.:
142522078
AWG 115
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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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Lo que más destaca
Practical Approach
Focuses on hands-on techniques and real-world applications, making complex Bayesian concepts accessible for practitioners and learners alike.
Comprehensive Coverage
Covers a wide range of topics in probabilistic modeling, ensuring a solid understanding of both foundational theories and advanced methods.
Python Integration
Utilizes Python for implementations, allowing readers to effectively use practical tools and libraries for their own Bayesian analysis projects.
Detalles del producto
- Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these libraries. Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Conduct Bayesian data analysis with step-by-step guidance Gain insight into a modern, practical, and computational approach to Bayesian statistical modeling Enhance your learning with best practices through sample problems and practice exercises Purchase of the print or Kindle book includes a free PDF eBook. Book Description The third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection. In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets. By the end of this book, you’ll understand probabilistic modeling and be able to design and implement Bayesian models for data science, with a strong foundation for more advanced study. *Email sign-up and proof of purchase required What you will learn Build probabilistic models using PyMC and Bambi Analyze and interpret probabilistic models with ArviZ Acquire the skills to sanity-check models and modify them if necessary Build better models with prior and posterior predictive checks Learn the advantages and caveats of hierarchical models Compare models and choose between alternative ones Interpret results and apply your knowledge to real-world problems Explore common models from a unified probabilistic perspective Apply the Bayesian framework's flexibility for probabilistic thinking Who this book is for If you are a student, data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and scientific libraries like NumPy is expected. Table of Contents Thinking Probabilistically Programming Probabilistically Hierarchical Models Modeling with Lines Comparing Models Modeling with Bambi Mixture Models Gaussian Processes Bayesian Additive Regression Trees Inference Engines Where to Go Next
| Publisher | Packt Publishing |
| Publication date | 31 Jan. 2024 |
| Edition | 3. |
| Language | English |
| Print length | 394 pages |
| ISBN-10 | 1805127160 |
| ISBN-13 | 978-1805127161 |
| Dimensions | 19.05 x 2.26 x 23.5 cm |
¿Quién debería comprarlo?
-
Data Science Students
Ideal for students learning advanced data analysis, offering practical applications of Bayesian methods in Python.
-
Statistical Researchers
Benefits researchers needing a practical guide for probabilistic modeling, enhancing their statistical analysis skills.
-
Machine Learning Practitioners
Perfect for ML professionals interested in incorporating Bayesian techniques into their predictive modeling workflows.
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Beginner Programmers
May be too advanced for those new to programming or Python, lacking foundational knowledge for understanding the content.
DESCRIPCIÓN DEL PRODUCTO
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Preguntas y respuestas de los clientes
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Pregunta:
Is prior statistical knowledge necessary to use this book?
Respuesta: No, the book is introductory and requires no previous statistical knowledge. -
Pregunta:
What Python libraries are covered in this book?
Respuesta: The book covers PyMC, ArviZ, Bambi, and more for Bayesian modeling. -
Pregunta:
Can I access an eBook version of the book?
Respuesta: Yes, the purchase of the print or Kindle book includes a free PDF eBook.
English edition Osvaldo Martin Format: Paperback Editorial Review
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Ventajas
- Clear and practical instructions
- Comprehensive coverage of topics
- Useful examples and applications
- Engaging writing style
- Great for beginners and experts
Desventajas
- Some advanced topics may require prior knowledge.
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AWG 115
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Características y beneficios
- 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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