Yao yuling (14 Ergebnisse)
Bayesian Workflow
Gelman, Andrew; Vehtari, Aki; McElreath, Richard; Simpson, Daniel; Margossian, Charles C.; Yao, Yuling; Kennedy, Lauren; Gabry, Jonah; Bürkner, Paul-Christian; Modrák, Martin; Barajas, Vianey Leos
- Softcover
Anbieter: Majestic Books, Hounslow, Vereinigtes KönigreichMajestic Books
Verkäufer/-in kontaktierenVerkäufer/-in mit 4 SternenZustand: Neu
EUR 62,17
EUR 7,61 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 3 verfügbar
Zustand: New.
Bayesian Workflow
Gelman, Andrew; Vehtari, Aki; McElreath, Richard; Simpson, Daniel; Margossian, Charles C.; Yao, Yuling; Kennedy, Lauren; Gabry, Jonah; Bürkner, Paul-Christian; Modrák, Martin; Barajas, Vianey Leos
- Softcover
Anbieter: California Books, Miami, FL, USACalifornia Books
Verkäufer/-in kontaktierenVerkäufer/-in mit 4 SternenZustand: Neu
EUR 70,30
Versand gratisVersand innerhalb von USAAnzahl: Mehr als 20 verfügbar
Zustand: New.
- Softcover
- Erstausgabe
Anbieter: Rarewaves USA, OSWEGO, IL, USARarewaves USA
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 77,10
Versand gratisVersand innerhalb von USAAnzahl: Mehr als 20 verfügbar
Paperback. Zustand: New. 1st. 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.FeaturesCovers all aspects of Bayesian statistical workflow, including model building, inference, validation, troubleshooting, and understandingDemonstrates iterative model development and computational problem-solving through real-world case studiesExplores computational challenges, calibration checking, and connections between modeling and computationHighlights the importance of checking models under diverse conditions to understand their limitations and improve their robustnessDiscusses how Bayesian principles apply to non-Bayesian methods in statistics and machine learningIncludes code snippets, exercises, and links to full datasets and code in R and Stan, with applicability to other programming environments like Python and JuliaThis 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.
Bayesian Workflow
Gelman, Andrew; Vehtari, Aki; McElreath, Richard; Simpson, Daniel; Margossian, Charles C.; Yao, Yuling; Kennedy, Lauren; Gabry, Jonah; Bürkner, Paul-Christian; Modrák, Martin; Barajas, Vianey Leos
- Softcover
Anbieter: Biblios, frankfurt am main, HESSE, DeutschlandBiblios
Verkäufer/-in kontaktierenVerkäufer/-in mit 4 SternenZustand: Neu
EUR 73,71
EUR 9,95 VersandVersand von Deutschland nach USAAnzahl: 3 verfügbar
Zustand: New.
Bayesian Workflow
Gelman, Andrew; Vehtari, Aki; McElreath, Richard; Simpson, Daniel; Margossian, Charles C.; Yao, Yuling; Kennedy, Lauren; Gabry, Jonah; Bürkner, Paul-Christian; Modrák, Martin; Barajas, Vianey Leos
- Softcover
Anbieter: Books Puddle, New York, NY, USABooks Puddle
Verkäufer/-in kontaktierenVerkäufer/-in mit 4 SternenZustand: Neu
EUR 86,61
EUR 3,45 VersandVersand innerhalb von USAAnzahl: 3 verfügbar
Zustand: New.
- Softcover
Anbieter: moluna, Greven, Deutschlandmoluna
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EUR 51,45
EUR 48,99 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Kartoniert / Broschiert. 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 .
- Softcover
- Erstausgabe
Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes KönigreichRarewaves.com USA
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 105,18
Versand gratisVersand von Vereinigtes Königreich nach USAAnzahl: Mehr als 20 verfügbar
Paperback. Zustand: New. 1st. 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.FeaturesCovers all aspects of Bayesian statistical workflow, including model building, inference, validation, troubleshooting, and understandingDemonstrates iterative model development and computational problem-solving through real-world case studiesExplores computational challenges, calibration checking, and connections between modeling and computationHighlights the importance of checking models under diverse conditions to understand their limitations and improve their robustnessDiscusses how Bayesian principles apply to non-Bayesian methods in statistics and machine learningIncludes code snippets, exercises, and links to full datasets and code in R and Stan, with applicability to other programming environments like Python and JuliaThis 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.
- Softcover
- Erstausgabe
Anbieter: Rarewaves USA United, OSWEGO, IL, USARarewaves USA United
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 79,30
EUR 43,20 VersandVersand innerhalb von USAAnzahl: Mehr als 20 verfügbar
Paperback. Zustand: New. 1st. 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.FeaturesCovers all aspects of Bayesian statistical workflow, including model building, inference, validation, troubleshooting, and understandingDemonstrates iterative model development and computational problem-solving through real-world case studiesExplores computational challenges, calibration checking, and connections between modeling and computationHighlights the importance of checking models under diverse conditions to understand their limitations and improve their robustnessDiscusses how Bayesian principles apply to non-Bayesian methods in statistics and machine learningIncludes code snippets, exercises, and links to full datasets and code in R and Stan, with applicability to other programming environments like Python and JuliaThis 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.
Bayesian Workflow
Gelman, Andrew; Vehtari, Aki; McElreath, Richard; Simpson, Daniel; Margossian, Charles C.; Yao, Yuling; Kennedy, Lauren; Gabry, Jonah; Bürkner, Paul-Christian; Modrák, Martin; Barajas, Vianey Leos
- Hardcover
Anbieter: Majestic Books, Hounslow, Vereinigtes KönigreichMajestic Books
Verkäufer/-in kontaktierenVerkäufer/-in mit 4 SternenZustand: Neu
EUR 157,84
EUR 7,61 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 3 verfügbar
Zustand: New.
Bayesian Workflow
Gelman, Andrew; Vehtari, Aki; McElreath, Richard; Simpson, Daniel; Margossian, Charles C.; Yao, Yuling; Kennedy, Lauren; Gabry, Jonah; Bürkner, Paul-Christian; Modrák, Martin; Barajas, Vianey Leos
- Hardcover
Anbieter: California Books, Miami, FL, USACalifornia Books
Verkäufer/-in kontaktierenVerkäufer/-in mit 4 SternenZustand: Neu
EUR 169,09
Versand gratisVersand innerhalb von USAAnzahl: Mehr als 20 verfügbar
Zustand: New.
- Softcover
- Erstausgabe
Anbieter: Rarewaves.com UK, London, Vereinigtes KönigreichRarewaves.com UK
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 99,48
EUR 76,08 VersandVersand von Vereinigtes Königreich nach USAAnzahl: Mehr als 20 verfügbar
Paperback. Zustand: New. 1st. 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.FeaturesCovers all aspects of Bayesian statistical workflow, including model building, inference, validation, troubleshooting, and understandingDemonstrates iterative model development and computational problem-solving through real-world case studiesExplores computational challenges, calibration checking, and connections between modeling and computationHighlights the importance of checking models under diverse conditions to understand their limitations and improve their robustnessDiscusses how Bayesian principles apply to non-Bayesian methods in statistics and machine learningIncludes code snippets, exercises, and links to full datasets and code in R and Stan, with applicability to other programming environments like Python and JuliaThis 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.
Bayesian Workflow
Gelman, Andrew; Vehtari, Aki; McElreath, Richard; Simpson, Daniel; Margossian, Charles C.; Yao, Yuling; Kennedy, Lauren; Gabry, Jonah; Bürkner, Paul-Christian; Modrák, Martin; Barajas, Vianey Leos
- Hardcover
Anbieter: Books Puddle, New York, NY, USABooks Puddle
Verkäufer/-in kontaktierenVerkäufer/-in mit 4 SternenZustand: Neu
EUR 182,56
EUR 3,45 VersandVersand innerhalb von USAAnzahl: 3 verfügbar
Zustand: New.
Bayesian Workflow
Gelman, Andrew; Vehtari, Aki; McElreath, Richard; Simpson, Daniel; Margossian, Charles C.; Yao, Yuling; Kennedy, Lauren; Gabry, Jonah; Bürkner, Paul-Christian; Modrák, Martin; Barajas, Vianey Leos
- Hardcover
Anbieter: Biblios, frankfurt am main, HESSE, DeutschlandBiblios
Verkäufer/-in kontaktierenVerkäufer/-in mit 4 SternenZustand: Neu
EUR 179,85
EUR 9,95 VersandVersand von Deutschland nach USAAnzahl: 3 verfügbar
Zustand: New.
- Hardcover
Anbieter: moluna, Greven, Deutschlandmoluna
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 172,62
EUR 48,99 VersandVersand von Deutschland nach USAAnzahl: Mehr als 20 verfügbar
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 .
