Yao yuling (14 résultats)
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
- Couverture souple
Vendeur : Majestic Books, Hounslow, Royaume-UniMajestic Books
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 61,59
EUR 7,59 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 3 disponible(s)
Etat : 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
- Couverture souple
Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 69,65
Frais de port gratuitsExpédition nationale : Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : New.
- Couverture souple
- Édition originale
Vendeur : Rarewaves USA, HEBRON, KY, Etats-UnisRarewaves USA
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 76,39
Frais de port gratuitsExpédition nationale : Etats-UnisQuantité disponible : Plus de 20 disponibles
Paperback. Etat : 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 an…d 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
- Couverture souple
Vendeur : Biblios, frankfurt am main, HESSE, AllemagneBiblios
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 73,71
EUR 9,95 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 3 disponible(s)
Etat : 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
- Couverture souple
Vendeur : Books Puddle, New York, NY, Etats-UnisBooks Puddle
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 85,81
EUR 3,42 expéditionExpédition nationale : Etats-UnisQuantité disponible : 3 disponible(s)
Etat : New.
- Couverture souple
- Édition originale
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-UniRarewaves.com USA
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 103,53
Frais de port gratuitsExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : Plus de 20 disponibles
Paperback. Etat : 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 an…d 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.
- Couverture souple
Vendeur : moluna, Greven, Allemagnemoluna
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 51,88
EUR 48,99 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 1 disponible(s)
Etat : 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 .
- Couverture souple
- Édition originale
Vendeur : Rarewaves USA United, HEBRON, KY, Etats-UnisRarewaves USA United
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 79,11
EUR 42,80 expéditionExpédition nationale : Etats-UnisQuantité disponible : Plus de 20 disponibles
Paperback. Etat : 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 an…d 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
- Couverture rigide
Vendeur : Majestic Books, Hounslow, Royaume-UniMajestic Books
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 156,37
EUR 7,59 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 3 disponible(s)
Etat : New.
- Couverture souple
- Édition originale
Vendeur : Rarewaves.com UK, London, Royaume-UniRarewaves.com UK
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 98,60
EUR 75,90 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : Plus de 20 disponibles
Paperback. Etat : 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 an…d 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
- Couverture rigide
Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 178,98
Frais de port gratuitsExpédition nationale : Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : 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
- Couverture rigide
Vendeur : Books Puddle, New York, NY, Etats-UnisBooks Puddle
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 180,86
EUR 3,42 expéditionExpédition nationale : Etats-UnisQuantité disponible : 3 disponible(s)
Etat : 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
- Couverture rigide
Vendeur : Biblios, frankfurt am main, HESSE, AllemagneBiblios
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 179,85
EUR 9,95 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 3 disponible(s)
Etat : New.
- Couverture rigide
Vendeur : moluna, Greven, Allemagnemoluna
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 168,95
EUR 48,99 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : 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 .
