Martin jonah (49 risultati)

dvd. Condizione: Used - Good.

Condizione: New.

Condizione: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.

Hardback. Condizione: New.

Hardback. Condizione: New.

Taschenbuch. Condizione: Wie neu. Gebraucht - Wie neu ehemaliges Archivexemplar; Stempel im Vorsatz, sehr guter Zustand -Die Odyssee einer Frau Shoshanna wächst in dem kleinen Ort Nazareth auf. Da sie als unrein gilt, wird sie von allen gemieden - nur nicht von Jeshua, von dem es heißt, dass er Wunder vollbringt. Bevor sie sich ihm anschließen kann, wird sie von Piraten entführt. Als Sklavin im fernen Griechenland träumt sie von Jeshua - und sie setzt alles daran, ihn wiederzusehen. Die spannende Geschichte einer Frau, die zu den ersten Christen gehört und die dramatischen Erlebnisse um die Kreuzigung Jesu miterlebt. Shoshanna, ein junges hübsches Mädchen, wächst unbeschwert in Nazareth auf bis ihr Onkel ihr eines Tages Gewalt antut. Fortan leidet sie unter ständigen Blutungen und wird als unrein gebrandmarkt. Einzig Jeshua, ein junger Mann aus der Nachbarschaft, steht zu ihr. Als er Nazareth verlässt und als Prediger durch das Land zieht, folgt Shoshanna ihm und bittet ihn, sie von ihrem Leiden zu heilen. Die Heilung gelingt, und Shoshanna entschließt sich, ihr Leben Jeshua zu weihen, doch da wird sie von Piraten gefangen genommen und nach Griechenland verschleppt. Eine lange aufregende Odyssee beginnt. 505 pp. Deutsch.…

Condizione: New.

kart. 1. Aufl. 504 S. ; 19 cm Buch in guter Erhaltung, Einband sauber und unbestoßen, Seiten hell und sauber, Schnitt: Mängelexemplar. Die Odyssee einer Frau Shoshanna wächst in dem kleinen Ort Nazareth auf. Da sie als unrein gilt, wird sie von allen gemieden - nur nicht von Jeshua, von dem es heißt, dass er Wunder vollbringt. Bevor sie sich ihm anschließen kann, wird sie von Piraten entführt. Als Sklavin im fernen Griechenland träumt sie von Jeshua - und sie setzt alles daran, ihn wiederzusehen. Die spannende Geschichte einer Frau, die zu den ersten Christen gehört und die dramatischen Erlebnisse um die Kreuzigung Jesu miterlebt. Shoshanna, ein junges hübsches Mädchen, wächst unbeschwert in Nazareth auf bis ihr Onkel ihr eines Tages Gewalt antut. Fortan leidet sie unter ständigen Blutungen und wird als unrein gebrandmarkt. Einzig Jeshua, ein junger Mann aus der Nachbarschaft, steht zu ihr. Als er Nazareth verlässt und als Prediger durch das Land zieht, folgt Shoshanna ihm und bittet ihn, sie von ihrem Leiden zu heilen. Die Heilung gelingt, und Shoshanna entschließt sich, ihr Leben Jeshua zu weihen, doch da wird sie von Piraten gefangen genommen und nach Griechenland verschleppt. Eine lange aufregende Odyssee beginnt. , Sprache: Deutsch Gewicht in Gramm: 396.…

Condizione: New.

Condizione: New.

Paperback. Condizione: 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.…

Condizione: New.

Paperback. Condizione: 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.…

Hardback. Condizione: New.

Taschenbuch. Condizione: Wie neu. Gebraucht - Wie neu ehemaliges Archivexemplar; Stempel im Vorsatz, sehr guter Zustand -Die Odyssee einer Frau Shoshanna wächst in dem kleinen Ort Nazareth auf. Da sie als unrein gilt, wird sie von allen gemieden - nur nicht von Jeshua, von dem es heißt, dass er Wunder vollbringt. Bevor sie sich ihm anschließen kann, wird sie von Piraten entführt. Als Sklavin im fernen Griechenland träumt sie von Jeshua - und sie setzt alles daran, ihn wiederzusehen. Die spannende Geschichte einer Frau, die zu den ersten Christen gehört und die dramatischen Erlebnisse um die Kreuzigung Jesu miterlebt. Shoshanna, ein junges hübsches Mädchen, wächst unbeschwert in Nazareth auf bis ihr Onkel ihr eines Tages Gewalt antut. Fortan leidet sie unter ständigen Blutungen und wird als unrein gebrandmarkt. Einzig Jeshua, ein junger Mann aus der Nachbarschaft, steht zu ihr. Als er Nazareth verlässt und als Prediger durch das Land zieht, folgt Shoshanna ihm und bittet ihn, sie von ihrem Leiden zu heilen. Die Heilung gelingt, und Shoshanna entschließt sich, ihr Leben Jeshua zu weihen, doch da wird sie von Piraten gefangen genommen und nach Griechenland verschleppt. Eine lange aufregende Odyssee beginnt. 505 pp. Deutsch.…

paperback. Condizione: New. In shrink wrap. Looks like an interesting title.

Couverture rigide. Condizione: Très bon. 1 vol.; 580 p.-16 p. de pl. ; 15 x 24 cm Broché couv. souple, coin sup. de la 1ère de couv. plissé; intérieur très frais; bon état 7F4. Martin, Pierre (19.-. ; traducteur). Traducteur (illustratore).

Condizione: Sehr gut. Auflage: 1. 505 Seiten minimale Lagerspuren, innen wie neu Q94 Sprache: Deutsch Gewicht in Gramm: 396 19,0 x 11,4 x 3,2 cm, Taschenbuch.

Kartoniert / Broschiert. Condizione: 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 .

Taschenbuch. Condizione: Very Good. Zustand: Sauberes Buch in gutem Zustand - Als Maengelexemplar gekennzeichnet - Werktaeglicher Versand - Rechnung mit ausgewiesener MwSt kommt automatisch per Mail .

Hardback. Condizione: New.

Paperback. Condizione: 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.…

Paperback. Condizione: 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.…

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: 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 .

Hardback. Condizione: New. 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.…

Hardback. Condizione: New. 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.…