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The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a new chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), expanded coverage of residual analysis in linear models, and more examples using real data.
This text investigates the foundational principles of probability and statistics by integrating both classical frequentist and Bayesian methodologies. Authored by Morris H. DeGroot and the Carnegie-Mellon University staff, the book provides a rigorous mathematical framework for understanding data analysis. It utilizes a structured approach to probability theory, moving from basic axioms to complex statistical inference and modeling techniques.
What You Will Find
Experts and educators frequently cite this work as a foundational text for undergraduate and graduate-level statistics courses. Readers often note the high level of mathematical rigor and the density of the prose, which requires a strong background in calculus to fully comprehend the material.
Page Count:
678
Publication Date:
1986-01-01
Publisher:
Addison-Wesley Longman, Incorporated
ISBN-10:
0201113678
ISBN-13:
9780201113679
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