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Mechanistic Home Range Analysis - Paul R. Moorcroft - Bog - Princeton University Press - Plusbog.dk

Mechanistic Home Range Analysis - Paul R. Moorcroft - Bog - Princeton University Press - Plusbog.dk

Spatial patterns of movement are fundamental to the ecology of animal populations, influencing their social organization, mating systems, demography, and the spatial distribution of prey and competitors. However, our ability to understand the causes and consequences of animal home range patterns has been limited by the descriptive nature of the statistical models used to analyze them. In Mechanistic Home Range Analysis , Paul Moorcroft and Mark Lewis develop a radically new framework for studying animal home range patterns based on the analysis of correlated random work models for individual movement behavior. They use this framework to develop a series of mechanistic home range models for carnivore populations. The authors'' analysis illustrates how, in contrast to traditional statistical home range models that merely describe pattern, mechanistic home range models can be used to discover the underlying ecological determinants of home range patterns observed in populations, make accurate predictions about how spatial distributions of home ranges will change following environmental or demographic disturbance, and analyze the functional significance of the movement strategies of individuals that give rise to observed patterns of space use. By providing researchers and graduate students of ecology and wildlife biology with a more illuminating way to analyze animal movement, Mechanistic Home Range Analysis will be an indispensable reference for years to come.

DKK 573.00
1

How to Make a Home - - Bog - Princeton University Press - Plusbog.dk

How to Make a Home - - Bog - Princeton University Press - Plusbog.dk

An entertaining and enlightening collection of ancient Roman writings about home design and decoration The idea that our homes can communicate professional as well as personal identities may seem as new as the work-from-home revolution. But it was second nature to the ancient Romans, for whom the home was in many ways the center of public and private life. Roman authors saw infinite practical and symbolic value in houses, and they have much to say about them. How to Make a Home presents some of the best Roman writings on houses—from buying and selling to designing and decorating.Edited and elegantly translated by Marden Fitzpatrick Nichols, How to Make a Home gathers selections by Cicero, Vitruvius, Seneca, and others, with the original Latin or Greek on facing pages. These writings reveal the pleasures and pitfalls of the Roman practice of making one’s home a cornerstone of self-expression. While the ideal home enshrined Roman virtues and could make a career, lavish building projects could lead to financial ruin and moral condemnation. These authors memorably describe such travails as deceptive staging, decorators run amok, know-it-all owners, unsupervised contractors, and buyer’s remorse. Along the way, they also explain why simplicity is bliss, privacy is for nobodies, a neglected house is a sign of a neglected soul, and much more.A unique and charming introduction to Roman domestic architecture and its cultural significance, How to Make a Home reveals that the obsession with house and home has a long and fascinating history.

DKK 170.00
1

Statistical Inference via Convex Optimization - Arkadi Nemirovski - Bog - Princeton University Press - Plusbog.dk

Statistical Inference via Convex Optimization - Arkadi Nemirovski - Bog - Princeton University Press - Plusbog.dk

This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory can be used to devise and analyze near-optimal statistical inferences. Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems—sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals—demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems. Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text.

DKK 758.00
1

Dark Matter - David J. E. Marsh - Bog - Princeton University Press - Plusbog.dk

Dark Matter - David J. E. Marsh - Bog - Princeton University Press - Plusbog.dk