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Stavka #:
91431911
Bayesian Analysis with Python: A practical guide to probabilistic modeling
Stavka #:
91431911
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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 Guidance
Offers hands-on examples and practical applications, making Bayesian concepts accessible and applicable to real-world scenarios, ideal for data scientists and statisticians seeking to enhance their analytical skills.
Comprehensive Coverage
Covers a wide range of topics, including advanced modeling techniques, ensuring readers gain a thorough understanding of probabilistic modeling and its applications across various fields.
Python Integration
Integrates Python libraries effectively, providing readers with the tools necessary to implement Bayesian methods in popular programming environments, thereby enhancing their programming and analytical capabilities.
Detalji o proizvodu
- 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 FeaturesConduct Bayesian data analysis with step-by-step guidanceGain insight into a modern, practical, and computational approach to Bayesian statistical modelingEnhance your learning with best practices through sample problems and practice exercisesPurchase of the print or Kindle book includes a free PDF eBook.Book DescriptionThe 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 requiredWhat you will learnBuild probabilistic models using PyMC and BambiAnalyze and interpret probabilistic models with ArviZAcquire the skills to sanity-check models and modify them if necessaryBuild better models with prior and posterior predictive checksLearn the advantages and caveats of hierarchical modelsCompare models and choose between alternative onesInterpret results and apply your knowledge to real-world problemsExplore common models from a unified probabilistic perspectiveApply the Bayesian framework's flexibility for probabilistic thinkingWho this book is forIf 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 ContentsThinking ProbabilisticallyProgramming ProbabilisticallyHierarchical ModelsModeling with LinesComparing ModelsModeling with BambiMixture ModelsGaussian ProcessesBayesian Additive Regression TreesInference EnginesWhere to Go Next
| Publisher | Packt Publishing |
| Publication date | January 31, 2024 |
| Edition | 3rd |
| Language | English |
| Print length | 394 pages |
| ISBN-10 | 1805127160 |
| ISBN-13 | 978-1805127161 |
| Item Weight | 1.49 pounds (680 grams) |
| Dimensions | 7.5 x 0.89 x 9.25 inches (19.1 x 2.3 x 23.5 cm) |
Who Should Buy?
-
Data Scientists
Ideal for data scientists looking to implement probabilistic models using Python for data analysis and decision-making.
-
Statisticians
Great for statisticians seeking to deepen their understanding of Bayesian methods and their applications in real-world scenarios.
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Students
Perfect for students in statistics or data science who need a practical guide to Bayesian analysis techniques and applications.
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Beginner Programmers
Not suitable for beginners unfamiliar with programming or basic concepts in statistics and probability.
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Casual Readers
May not appeal to casual readers seeking light content, as it delves deeply into technical aspects of Bayesian analysis.
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Business Executives
Not an ideal resource for business executives who need quick solutions rather than in-depth statistical understanding and modeling.
OPIS PROIZVODA
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Pitanja i odgovori kupaca
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pitanje:
What is Bayesian Analysis with Python about?
odgovor: Bayesian Analysis with Python is a practical guide focused on probabilistic modeling using Python programming. It provides a comprehensive introduction to the principles and techniques of Bayesian data analysis. This book is ideal for statisticians, data scientists, and researchers who wish to understand the Bayesian approach in a hands-on manner, utilizing Python libraries such as PyMC3. Readers will gain insights into model specification, evaluation, and advanced topics like Markov Chain Monte Carlo methods, enhancing their data analysis skills across various domains. -
pitanje:
Who is the target audience for this book?
odgovor: This book is specifically tailored for data scientists, statisticians, and researchers who are engaged in statistical modeling and data analysis. Individuals with a foundational understanding of statistics and Python programming will find this book particularly beneficial. It strikes a balance between theory and practical application, making it suitable for both academic and professional environments. Those looking to deepen their understanding of Bayesian methods and apply them to real-world data will find it invaluable. -
pitanje:
What programming skills do I need to follow this book effectively?
odgovor: To effectively follow Bayesian Analysis with Python, a basic understanding of Python programming is essential. Familiarity with libraries like NumPy and pandas will be helpful as they provide the necessary data manipulation capabilities. Although the book does introduce Bayesian concepts, having prior experience in coding will allow readers to implement the models more fluently, allowing for a smoother learning experience. Beginners may need to brush up on their Python skills to fully engage with the content. -
pitanje:
What particular topics does the book cover?
odgovor: The book delves into various topics including model specification, Bayesian inference, Markov Chain Monte Carlo methods, and hierarchical models. It also discusses practical applications of Bayesian analysis in real-world scenarios, such as predictive modeling and decision-making under uncertainty. Each chapter builds on the last, guiding readers through the complexities of Bayesian statistical methods in a logical sequence. This comprehensive coverage makes it suitable for both novices and experienced practitioners looking to enhance their Bayesian knowledge. -
pitanje:
Is this book suitable for beginners in Bayesian analysis?
odgovor: Yes, Bayesian Analysis with Python is designed to accommodate beginners who may not have extensive experience with Bayesian methods. It introduces concepts gradually, making complex ideas more digestible. While some prior knowledge of statistics and Python is advantageous, the book provides adequate theoretical explanations alongside practical coding examples. This approach ensures that newcomers can grasp the essential concepts and methodologies needed to apply Bayesian analysis effectively. -
pitanje:
How does this book compare to previous editions?
odgovor: The third edition of Bayesian Analysis with Python brings updated methodologies, new case studies, and enhanced examples that reflect the latest advancements in Bayesian statistical techniques. It addresses feedback from readers of previous editions, refining explanations and expanding on topics that benefit from a more in-depth exploration. This makes the third edition a contemporary resource that not only updates readers on recent developments but also ensures they stay relevant in the fast-evolving field of data science. -
pitanje:
Can this book help me with practical applications of Bayesian analysis?
odgovor: Absolutely. Bayesian Analysis with Python emphasizes practical applications and real-world case studies, allowing readers to see how Bayesian methods can be applied across various domains. The book includes hands-on projects that guide readers through the implementation of Bayesian models in practical scenarios such as marketing analysis, clinical trials, and machine learning. By the end of the book, readers will be equipped with the knowledge and skills necessary to tackle their own data analysis problems using Bayesian techniques. -
pitanje:
What additional resources are included with the book?
odgovor: The book often includes supplementary resources such as Jupyter notebooks, datasets, and code snippets that facilitate hands-on learning. Access to an online community or forum for readers of the book may also be provided, allowing individuals to ask questions, share insights, and collaborate on projects. These resources enhance the learning experience by providing practical tools and a supportive network, making it easier for readers to apply the concepts learned in the text. -
pitanje:
Where can I buy Bayesian Analysis with Python: A practical guide to probabilistic modeling 3rd ed. Edition in Bosnia and Herzegovina?
odgovor: You can purchase Bayesian Analysis with Python: A practical guide to probabilistic modeling 3rd ed. Edition on Ubuy, which services customers in Bosnia and Herzegovina. Ubuy offers a convenient platform to find the book along with fast navigation and secure payment options. By choosing Ubuy, you ensure a reliable shopping experience and access to the latest edition, helping you enhance your statistical and programming skills efficiently. -
pitanje:
Does this book offer coding examples with Python?
odgovor: Yes, Bayesian Analysis with Python includes numerous coding examples using Python to illustrate Bayesian concepts and methods. Each chapter features practical exercises that allow readers to implement models and see results in real-time. By providing code snippets and detailed explanations, the book equips readers with the tools needed to understand and apply Bayesian analysis in their own projects. This hands-on approach fosters a deep understanding of the subject matter and prepares readers for real-world data analysis challenges.
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Značajke i prednosti
- 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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