الصفحة 1
الصفحة 1
Modelli Matematici in Biologia = Mathematical Models in Biology
This text is addressed first of all to the students of the Specialist Degrees in Biology of the Universities, but it will also be of interest to students of Natural Sciences and Medicine. The topics covered include the most classic mathematical models of biological phenomena (population dynamics, spread of infectious diseases, simple physiology models), but a relevant part of the text is dedicated to the mathematical approach to the theory of natural evolution. The only prerequisites required of the reader are those provided by the basic courses of Mathematics of the Bachelor's Degree in Biology, Natural Sciences or Medicine.
Génetique statistique = Statistical genetics
Presents the main statistical tools useful in genetics: significance tests, analysis methods based on the likelihood function, EM algorithm, modeling, analysis of variance, hierarchical classifications, multiple comparisons, etc. All of them shed light on a number of biological phenomena such as carcinogenesis, population genetics, Hardy-Weinberg equilibrium, natural selection, mutations, heredity, coalescence processes, and even evolution. This book is intended for mathematicians and biologists alike. Written with a great concern for clarity, it is also accessible to non-specialists who will be able, thanks to it, to strengthen their theoretical base and above all to develop their know-how through very concrete applications.
Linear and Generalized Linear Mixed Models and Their Applications
This book covers two major classes of mixed effects models—linear mixed models and generalized linear mixed models—and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.
