foundations bayesian statistics data von agresti alan (23 Ergebnisse)

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Zustand: New. Alan Agresti, Distinguished Professor Emeritus at the University of Florida, is the author of seven books, and has presented short courses in 35 countries. His awards include an honorary doctorate from De Montfort University (UK) and Statistician .

Taschenbuch. Zustand: Neu. Foundations of Bayesian Statistics for Data Scientists | With R and Python | Alan Agresti (u. a.) | Taschenbuch | Chapman & Hall/CRC Texts in Statistical Science | Einband - flex.(Paperback) | Englisch | 2026 | Taylor & Francis Ltd | EAN 9781041202929 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

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Zustand: New. Alan Agresti, Distinguished Professor Emeritus at the University of Florida, is the author of seven books, and has presented short courses in 35 countries. His awards include an honorary doctorate from De Montfort University (UK) and Statistician .

Hardcover. Zustand: Brand New. 456 pages. 10.00x7.00 inches. In Stock.

Paperback. Zustand: new. Paperback. This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science. It is designed as a textbook for advanced undergraduate and masters students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics.The reader should be familiar with calculus and should have taken a statistical inference Statistics course covering the basic rules of probability, probability distributions and expectations, as well as the fundamentals of the traditional, frequentist approach to statistics, including sampling distributions, likelihood functions, basic inferential methods such as point estimation, confidence intervals, significance tests, and linear regression models.Key Features: Uses real world data examples and contains numerous exercises. Includes software appendices in R and Python. Offers slides, labs, and other materials on the books website.Each chapter begins with a brief review of the primary frequentist methods for its topic before introducing corresponding Bayesian methods. This book presents some substantive theory as well as the methods, and is therefore intended for a reader who wishes to understand Bayesian methods rather than merely apply them. The focus is not just on presenting statistical methodologies but also on demonstrating how to implement them with modern software, emphasizing appropriate simulation methods. This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science. It is designed as a textbook for advanced undergraduate and masters students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Zustand: new. Hardcover. This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science. It is designed as a textbook for advanced undergraduate and masters students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics.The reader should be familiar with calculus and should have taken a statistical inference Statistics course covering the basic rules of probability, probability distributions and expectations, as well as the fundamentals of the traditional, frequentist approach to statistics, including sampling distributions, likelihood functions, basic inferential methods such as point estimation, confidence intervals, significance tests, and linear regression models.Key Features: Uses real world data examples and contains numerous exercises. Includes software appendices in R and Python. Offers slides, labs, and other materials on the books website.Each chapter begins with a brief review of the primary frequentist methods for its topic before introducing corresponding Bayesian methods. This book presents some substantive theory as well as the methods, and is therefore intended for a reader who wishes to understand Bayesian methods rather than merely apply them. The focus is not just on presenting statistical methodologies but also on demonstrating how to implement them with modern software, emphasizing appropriate simulation methods. This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science. It is designed as a textbook for advanced undergraduate and masters students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Zustand: new. Paperback. This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science. It is designed as a textbook for advanced undergraduate and masters students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics.The reader should be familiar with calculus and should have taken a statistical inference Statistics course covering the basic rules of probability, probability distributions and expectations, as well as the fundamentals of the traditional, frequentist approach to statistics, including sampling distributions, likelihood functions, basic inferential methods such as point estimation, confidence intervals, significance tests, and linear regression models.Key Features: Uses real world data examples and contains numerous exercises. Includes software appendices in R and Python. Offers slides, labs, and other materials on the books website.Each chapter begins with a brief review of the primary frequentist methods for its topic before introducing corresponding Bayesian methods. This book presents some substantive theory as well as the methods, and is therefore intended for a reader who wishes to understand Bayesian methods rather than merely apply them. The focus is not just on presenting statistical methodologies but also on demonstrating how to implement them with modern software, emphasizing appropriate simulation methods. This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science. It is designed as a textbook for advanced undergraduate and masters students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback / softback. Zustand: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Hardcover. Zustand: new. Hardcover. This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science. It is designed as a textbook for advanced undergraduate and masters students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics.The reader should be familiar with calculus and should have taken a statistical inference Statistics course covering the basic rules of probability, probability distributions and expectations, as well as the fundamentals of the traditional, frequentist approach to statistics, including sampling distributions, likelihood functions, basic inferential methods such as point estimation, confidence intervals, significance tests, and linear regression models.Key Features: Uses real world data examples and contains numerous exercises. Includes software appendices in R and Python. Offers slides, labs, and other materials on the books website.Each chapter begins with a brief review of the primary frequentist methods for its topic before introducing corresponding Bayesian methods. This book presents some substantive theory as well as the methods, and is therefore intended for a reader who wishes to understand Bayesian methods rather than merely apply them. The focus is not just on presenting statistical methodologies but also on demonstrating how to implement them with modern software, emphasizing appropriate simulation methods. This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science. It is designed as a textbook for advanced undergraduate and masters students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science. It is designed as a textbook for advanced undergraduate and masters students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics.The reader should be familiar with calculus and should have taken a statistical inference Statistics course covering the basic rules of probability, probability distributions and expectations, as well as the fundamentals of the traditional, frequentist approach to statistics, including sampling distributions, likelihood functions, basic inferential methods such as point estimation, confidence intervals, significance tests, and linear regression models.Key Features: Uses real world data examples and contains numerous exercises. Includes software appendices in R and Python. Offers slides, labs, and other materials on the books website.Each chapter begins with a brief review of the primary frequentist methods for its topic before introducing corresponding Bayesian methods. This book presents some substantive theory as well as the methods, and is therefore intended for a reader who wishes to understand Bayesian methods rather than merely apply them. The focus is not just on presenting statistical methodologies but also on demonstrating how to implement them with modern software, emphasizing appropriate simulation methods. This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science. It is designed as a textbook for advanced undergraduate and masters students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Zustand: new. Hardcover. This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science. It is designed as a textbook for advanced undergraduate and masters students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics.The reader should be familiar with calculus and should have taken a statistical inference Statistics course covering the basic rules of probability, probability distributions and expectations, as well as the fundamentals of the traditional, frequentist approach to statistics, including sampling distributions, likelihood functions, basic inferential methods such as point estimation, confidence intervals, significance tests, and linear regression models.Key Features: Uses real world data examples and contains numerous exercises. Includes software appendices in R and Python. Offers slides, labs, and other materials on the books website.Each chapter begins with a brief review of the primary frequentist methods for its topic before introducing corresponding Bayesian methods. This book presents some substantive theory as well as the methods, and is therefore intended for a reader who wishes to understand Bayesian methods rather than merely apply them. The focus is not just on presenting statistical methodologies but also on demonstrating how to implement them with modern software, emphasizing appropriate simulation methods. This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science. It is designed as a textbook for advanced undergraduate and masters students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.