(a) To appreciate how Brook's Lemma works, suppose Y1 and Y2 are both binary variables, and that their joint distribution is defined through conditional ... that is, bij > 0, ∑ j bij ≤ 1 for all i, and ∑ j bij < 1 for at least one i.
It is clear that given p(y1 ,j≠i), i=1,...,n, are uniquely determined. Brook's Lemma proves the converse and, in fact, enables us to constructively retrieve the unique joint distribution determined by these full conditionals.
This handbook offers a wide-ranging overview of state-of-the-art approaches to determine the relationships between health and various risk factors, empowering researchers and policy makers to tackle public health problems.
The book concludes with accessible treatments of advanced topics, such as linear iterative systems, convergence of matrices, more general vector spaces, linear transformations, and Hilbert spaces.
Bayesian detection of clusters and discontinuities in disease maps. Biometrics 56, 13–21. ... Modelling disease incidence data with spatial and spatio-temporal Dirichlet process mixtures. ... Disease Mapping with WinBUGS and MLwiN.
Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis,
Assuming no prior knowledge of linear algebra, this self-contained text offers a gradual exposition to linear algebra without sacrificing the rigor of the subject.
Keep Up to Date with the Evolving Landscape of Space and Space-Time Data Analysis and ModelingSince the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data.