Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics.
Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers:
This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Ben Lambert is a researcher at Imperial College London where he works on the epidemiology of malaria. He has worked in applied statistical inference for about a decade, formerly at the University of Oxford, and is the author of over 500 online lectures on econometrics and statistics. He also somewhat strangely went to school in Thomas Bayes’ home town for many years, Tunbridge Wells.
Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: An introduction to probability and Bayesian inference Understanding Bayes¿ rule Nuts and bolts of Bayesian analytic methods Computational Bayes and real-world Bayesian analysis Regression analysis and hierarchical methods This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Condizione: Very Good. : Esta guía está diseñada para estudiantes que se inician en el campo de la estadística bayesiana. Con un enfoque accesible y un lenguaje amigable, el libro introduce conceptos clave de manera gradual, facilitando la comprensión y el uso del software R y Stan. Cubre desde la introducción a la probabilidad y la inferencia bayesiana hasta métodos analíticos bayesianos computacionales y análisis bayesianos del mundo real, incluyendo análisis de regresión y métodos jerárquicos. Este libro ayuda a los estudiantes a desarrollar la confianza y las habilidades estadísticas necesarias para aplicar la fórmula bayesiana en la práctica. EAN: 9781473916364 Tipo: Libros Categoría: Ciencias|Educación|Tecnología Título: A Student's Guide to Bayesian Statistics Autor: Ben Lambert Editorial: SAGE Publications Ltd Idioma: en Páginas: 520 Formato: tapa blanda. Codice articolo Happ-2026-08-04-f4d2949e
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Paperback. Condizione: New. Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: An introduction to probability and Bayesian inferenceUnderstanding Bayes' rule Nuts and bolts of Bayesian analytic methodsComputational Bayes and real-world Bayesian analysisRegression analysis and hierarchical methods This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses. Codice articolo LU-9781473916364
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