Fisheries science is an applied field. A biologist working on a depleted population might need to forecast the population trajectory or examine which factors are limiting recovery. For an exploited population, a biologist might investigate whether t...Lasīt vairāk
Fisheries science is an applied field. A biologist working on a depleted population might need to forecast the population trajectory or examine which factors are limiting recovery. For an exploited population, a biologist might investigate whethe...Lasīt vairāk
A fascinating, witty, and perspective-shifting (Oliver Burkeman, New York Times bestselling author) tour of Bayess theorem and its global impact on modern life from the acclaimed science writer and author of The Rational...Lasīt vairāk
Meta-analysis is the statistical combination of previously conducted studies, often from summary statistics but sometimes with individual participant data. It is widespread in life sciences and is gaining popularity in economics and beyond. In many...Lasīt vairāk
Provides an approachable introduction for researchers who are new to Bayes, meta-analysis, or both. There is an emphasis on hands-on learning using a variety of software packages. this book is extremely timely not just a technical...Lasīt vairāk
This book offers a comprehensive discussion of the Bayesian inference framework and demonstrates why this probabilistic approach is ideal for tackling the various modelling pro...Lasīt vairāk
This book delves into scalable Bayesian statistical methods designed to tackle the challenges posed by big data. It explores a variety of divide-and-conquer and subsampling techniques, seamlessly integrating these scalable methods into a broad spe...Lasīt vairāk
Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock
Sērija : Institute of Mathematical Statistics Monographs
(Izdošanas datums: 05-Jun-2025, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781009288446)
An intuitive introduction to advanced topics in Markov chain Monte Carlo (MCMC), presenting cutting-edge developments that address the crucial issue of scalability. It could form the basis for a graduate-level course and will be a valuable resource f...Lasīt vairāk
(Izdošanas datums: 03-Jun-2025, Hardback, Izdevniecība: Princeton University Press, ISBN-13: 9780691250120)
A fully updated and expanded edition of the essential primer on Bayesian modeling for ecologistsUniquely suited to deal with complexity in a statistically coherent way, Bayesian modeling has become an indispensable tool for ecological research. This...Lasīt vairāk
(Izdošanas datums: 01-Jun-2025, Other book format, Izdevniecība: Princeton University Press, ISBN-13: 9780691271453)
A fully updated and expanded edition of the essential primer on Bayesian modeling for ecologistsUniquely suited to deal with complexity in a statistically coherent way, Bayesian modeling has become an indispensable tool for ecological research. This...Lasīt vairāk
This book is a rigorous but practical presentation of the Bayesian techniques of uncertainty quantification, with applications in R. This volume includes mathematical arguments at the level necessary to make the presentation rigorous and the assum...Lasīt vairāk
Bayesian Statistics: The Basics provides a comprehensive yet accessible introduction to Bayesian statistics, specifically tailored for any researcher with an interest in statistical methods. It covers the theoretical foundations of Bayesian inferenc...Lasīt vairāk
Bayesian Statistics provides a comprehensive yet accessible introduction to Bayesian statistics, specifically tailored for any researcher with an interest in statistical methods. It covers the theoretical foundations of Bayesian inference,...Lasīt vairāk
A fascinating intellectual history that takes a comparatively little-known but important idea and shows how it affects huge areas of our lives...Lasīt vairāk
Bayesian decision theory is a mathematical framework that models reasoning and decision-making under uncertain conditions. The Bayesian paradigm originated as a theory of how people should operate, not a theory of how they actually operate. Neverthel...Lasīt vairāk
(Izdošanas datums: 30-Jan-2025, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781009517805)
Bayesian decision theory is a mathematical framework that models reasoning and decision-making under uncertain conditions. The Bayesian paradigm originated as a theory of how people should operate, not a theory of how they actually operate. Neverthel...Lasīt vairāk
Several important topics in spatial and spatio-temporal statistics developed in the last 15 years have not received enough attention in textbooks. Aims to fill some of this gap by providing an overview of a variety of recently proposed approaches...Lasīt vairāk
This up-to-date overview of Bayesian nonparametric statistics provides both an introduction to the field and coverage of recent research topics, including deep neural networks, high-dimensional models and multiple testing, Bernstein-von Mises theo...Lasīt vairāk
The book introduces a series of state-of-art Bayesian models that can be used to understand and predict spatially structured population dynamics in our changing world. Several chapters are devoted to introducing models that utilize detection/non-d...Lasīt vairāk
Sērija : Elements in Quantitative and Computational Methods for the Social Sciences
(Izdošanas datums: 24-Oct-2024, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781009494694)
In this Element, the authors introduce Bayesian probability and inference for social science students and practitioners starting from the absolute beginning and walk readers steadily through the Element. No previous knowledge is required other than t...Lasīt vairāk
In this Element, the authors introduce Bayesian probability and inference for social science students and practitioners starting from the absolute beginning and walk readers steadily through the Element. No previous knowledge is required other than t...Lasīt vairāk
This book is a compilation of unpublished papers written by Jean-Marie Rolin (with several co-authors) on nonparametric bayesian estimation. Jean-Marie was professor of statistics at University of Louvain and died on November 5th, 2018. He made im...Lasīt vairāk
Since the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information...Lasīt vairāk
Offering a step-by-step approach for applying the Nonparametric Method with the Bayesian Approach to model complex relationships occurring in Reliability Engineering, Quality Management, and Operations Research, it also discusses survival and cens...Lasīt vairāk
