Vehtari aki (20 Ergebnisse)

Zustand: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Zustand: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Paperback. Zustand: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Zustand: New. In English.

Hardcover. Zustand: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Paperback. Zustand: Brand New. 368 pages. 10.00x7.00x0.77 inches. In Stock.

Taschenbuch. Zustand: Neu. Neuware - This book provides statistics instructors and students with complete classroom material for a one- or two-semester course on applied regression and causal inference. It is built around 52 stories, 52 class-participation activities, 52 hands-on computer demonstrations, and 52 discussion problems that allow instructors and students to explore in a fun way the real-world complexity of the subject. The book fosters an engaging 'flipped classroom' environment with a focus on visualization and understanding. The book provides instructors with frameworks for self-study or for structuring the course, along with tips for maintaining student engagement at all levels, and practice exam questions to help guide learning. Designed to accompany the authors' previous textbook Regression and Other Stories, its modular nature and wealth of material allow this book to be adapted to different courses and texts or be used by learners as a hands-on workbook.…

Zustand: New.

hardcover. Zustand: Very Good. Cover and edges may have some wear.

Zustand: New. In English.

Paperback. Zustand: Brand New. 560 pages. 9.50x7.25x1.25 inches. In Stock.

Zustand: New. Andrew Gelman is a professor of statistics and political science at Columbia UniversityAki Vehtari is a professor of computer science at Aalto UniversityRichard McElreath is the director of the Max Planck Institute for .

Paperback. Zustand: Brand New. 544 pages. 10.00x7.00x10.00 inches. In Stock.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New.

Hardcover. Zustand: Brand New. 560 pages. 9.75x7.50x1.25 inches. In Stock.

Zustand: New. Andrew Gelman is a professor of statistics and political science at Columbia UniversityAki Vehtari is a professor of computer science at Aalto UniversityRichard McElreath is the director of the Max Planck Institute for .

Hardcover. Zustand: Brand New. 544 pages. 10.00x7.00x10.00 inches. In Stock.

Buch. Zustand: Neu. Neuware - Bayesian statistics and statistical practice have evolved over the years, driven by advancements in theory, methods, and computational tools. Bayesian Workflow explores the intricate workflows of applied Bayesian statistics, aiming to uncover the tacit knowledge often overlooked in published papers and textbooks. By systematizing the process of Bayesian model development, the book seeks to improve applied analyses and inspire future innovations in theory, methods, and software. It emphasizes the importance of iterative model building, model checking, computational troubleshooting, and simulated-data experimentation, offering a comprehensive perspective on statistical analysis.Through detailed examples and practical guidance, the book bridges the gap between theory and application, empowering practitioners and researchers to navigate the complexities of Bayesian inference. It is not a checklist or cookbook but a flexible framework for understanding and resolving challenges in statistical modeling and decision-making under uncertainty.Features - Covers all aspects of Bayesian statistical workflow, including model building, inference, validation, troubleshooting, and understanding - Demonstrates iterative model development and computational problem-solving through real-world case studies - Explores computational challenges, calibration checking, and connections between modeling and computation - Highlights the importance of checking models under diverse conditions to understand their limitations and improve their robustness - Discusses how Bayesian principles apply to non-Bayesian methods in statistics and machine learning - Includes code snippets, exercises, and links to full datasets and code in R and Stan, with applicability to other programming environments like Python and Julia This book is designed for practitioners of applied Bayesian statistics, particularly users of probabilistic programming languages such as Stan, as well as developers of methods and software tailored to these users. It also targets researchers in Bayesian theory and methods, offering insights into understudied aspects of statistical workflows. Instructors and students will find adaptable exercises and case studies to enhance their learning experience. Beyond Bayesian inference, the book's principles are relevant to users of non-Bayesian methods, making it a valuable resource for statisticians, data scientists, and machine learning professionals seeking to improve their modeling and decision-making processes.…