Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 5)

✍ Scribed by Thomas Leonard, John S. J. Hsu

Publisher: Cambridge University Press
Year: 1999
Tongue: English
Leaves: 346
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

This exposition of the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students is unique in presenting its subject with a practical flavor and an emphasis on mainstream statistics. It shows how to infer scientific, medical, and social conclusions from numerical data. The authors draw on many years of experience with practical and research programs and describe many new statistical methods, not available elsewhere. It will be essential reading for all statisticians, statistics students, and related interdisciplinary researchers.

📜 SIMILAR VOLUMES

Random Graphs and Complex Networks (Camb

📁 Random Graphs and Complex Networks (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 43)

✍ Remco van der Hofstad 📂 Library 📅 2016 🏛 Cambridge University Press 🌐 English

<span>This rigorous introduction to network science presents random graphs as models for real-world networks. Such networks have distinctive empirical properties and a wealth of new models have emerged to capture them. Classroom tested for over ten years, this text places recent advances in a unifie

Empirical Processes in M-Estimation (Cam

📁 Empirical Processes in M-Estimation (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 6)

✍ Sara A. van de Geer 📂 Library 📅 2009 🏛 Cambridge University Press 🌐 English

<span>The theory of empirical processes provides valuable tools for the development of asymptotic theory in (nonparametric) statistical models, and makes it possible to give a unified treatment of various models. This book reveals the relation between the asymptotic behavior of M-estimators and the

Semiparametric Regression (Cambridge Ser

📁 Semiparametric Regression (Cambridge Series in Statistical and Probabilistic Mathematics)

✍ David Ruppert, M. P. Wand, R. J. Carroll 📂 Library 📅 2003 🏛 Cambridge University Press 🌐 English

Statistical Hypothesis Testing in Contex

📁 Statistical Hypothesis Testing in Context: Volume 52: Reproducibility, Inference, and Science (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 52)

✍ Michael P. Fay, Erica H. Brittain 📂 Library 📅 2022 🏛 Cambridge University Press 🌐 English

<span>Fay and Brittain present statistical hypothesis testing and compatible confidence intervals, focusing on application and proper interpretation. The emphasis is on equipping applied statisticians with enough tools - and advice on choosing among them - to find reasonable methods for almost any p

Advances in Multivariate Statistical Met

📁 Advances in Multivariate Statistical Methods (Statistical Science and Interdisciplinary Research) (Statistical Science and Interdisciplinary Research: Platinum Juliee Series)

✍ Ashis Sengupta 📂 Library 📅 2009 🏛 World Scientific Publishing Company 🌐 English

This volume contains a collection of research articles on multivariate statistical methods, encompassing both theoretical advances and emerging applications in a variety of scientific disciplines. It serves as a tribute to Professor S N Roy, an eminent statistician who has made seminal contributions

Random Graphs and Complex Networks: Volu

📁 Random Graphs and Complex Networks: Volume 2 (Cambridge Series in Statistical and Probabilistic Mathematics)

✍ Remco van der Hofstad 📂 Library 📅 2024 🏛 Cambridge University Press 🌐 English

<span>Complex networks are key to describing the connected nature of the society that we live in. This book, the second of two volumes, describes the local structure of random graph models for real-world networks and determines when these models have a giant component and when they are small-, and u