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103564563
Bayesian Analysis with Python: A practical guide to probabilistic modeling
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Stavka #:
103564563
BAM 172
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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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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.
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 | 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.
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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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