Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 31-Aug-2025, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781009521437)
Random graphs, also known as complex networks, model the structure of a population. The spread of opinions and diseases on these structures is much different from the usual ODE models. This book will be of interest to mathematicians, computer scienti...Lasīt vairāk
This extensive revision of the 2007 book Random Graph Dynamics, covering the current state of mathematical research in the field, is ideal for researchers and graduate students. It considers a small number of types of graphs, primarily the configur...Lasīt vairāk
Mikis D. Stasinopoulos, Thomas Kneib, Nadja Klein, Andreas Mayr, Gillian Z. Heller
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 29-Feb-2024, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781009410069)
An emerging field in statistics, distributional regression facilitates the modelling of the complete conditional distribution, rather than just the mean. This book introduces generalized additive models for location, scale and shape (GAMLSS) one of...Lasīt vairāk
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 08-Feb-2024, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781107174009)
The first book to cover local convergence, this text discusses the local and global structure of random graph models for complex networks. Featuring examples of real-world networks for motivation and numerous exercises to build experience, it will be...Lasīt vairāk
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 18-Jan-2024, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781009305112)
Providing a graduate-level introduction to discrete probability and its applications, this book develops a toolkit of essential techniques for analysing stochastic processes on graphs, other random discrete structures, and algorithms. Topics covered...Lasīt vairāk
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 09-Jun-2022, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781316511732)
Heavy tails - extreme events more common than expected - are everywhere, but they are still treated as mysterious and confusing because the necessary mathematical models are not widely known. For the first time, this book provides a rigorous introduc...Lasīt vairāk
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 05-May-2022, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781108423564)
With over 60 years of applied experience, Fay and Brittain present hypothesis testing and compatible confidence intervals, emphasize strategies to address the reproducibility crisis, and provide methods for proper causal interpretation in scientific...Lasīt vairāk
This authoritative state-of-the-art account of probability on networks for graduate students and researchers in mathematics, statistics, computer science, and engineering, brings together sixty years of research, including many developments where the...Lasīt vairāk
High-dimensional and nonparametric statistical models are ubiquitous in modern data science. This book develops a mathematically coherent and objective approach to statistical inference in such models, with a focus on function estimation problems ari...Lasīt vairāk
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 19-Mar-2020, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781107028142)
Spectral analysis is widely used to interpret time series collected in diverse areas. This book covers the statistical theory behind spectral analysis and provides data analysts with the tools needed to transition theory into practice. Actual time se...Lasīt vairāk
Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 25-Jul-2019, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781108494205)
Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observati...Lasīt vairāk
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 18-Apr-2019, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781108473682)
The new edition of this lively but rigorous introduction to measure theoretic probability theory, designed for use in a graduate course, contains a new chapter on multidimensional Brownian motion and its relationship to partial differential equations...Lasīt vairāk
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 21-Feb-2019, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781108498029)
Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This...Lasīt vairāk
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 27-Sep-2018, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781108415194)
High-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it lends itsel...Lasīt vairāk
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 12-Apr-2018, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781107028289)
Aimed at statisticians and machine learners, this retooling of statistical theory asserts that high-quality prediction should be the guiding principle of modeling and learning from data, then shows how. The fully predictive approach to statistical pr...Lasīt vairāk
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 26-Jun-2017, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9780521878265)
Written by top researchers, this self-contained text is the authoritative account of Bayesian nonparametrics, a nearly universal framework for inference in statistics and machine learning, with practical use in all areas of science, including economi...Lasīt vairāk
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 18-Apr-2017, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781107039469)
This modern and comprehensive guide to long-range dependence and self-similarity starts with rigorous coverage of the basics, then moves on to cover more specialized, up-to-date topics central to current research. These topics concern, but are not li...Lasīt vairāk
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 20-Jan-2017, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781107160156)
Starting around the late 1950s, several research communities began relating the geometry of graphs to stochastic processes on these graphs. This book, twenty years in the making, ties together research in the field, encompassing work on percolation,...Lasīt vairāk
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 22-Dec-2016, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9781107172876)
This classroom-tested text is the definitive introduction to the mathematics of network science, featuring examples and numerous exercises....Lasīt vairāk
Sērija : Cambridge Series in Statistical and Probabilistic Mathematics
(Izdošanas datums: 24-Feb-2016, Hardback, Izdevniecība: Cambridge University Press, ISBN-13: 9780521861601)
This lively book lays out a methodology of confidence distributions and puts them through their paces. Among other merits they lead to optimal combinations of con dence from different sources of information, and they can make complex models amenable...Lasīt vairāk
