Wheat, which is the second most important cereal crop in the world, is being grown in a wide range of climates over an area of about 228 945 thou­ sand ha with a production of about 535 842 MT in the world. Bread wheat (Triticum aestivum L. ) accounts for 80% of the wheat consumption, howe­ ver, it is attacked by a large number of pests and pathogens; rusts and smuts cause enormous damage to the crop and reduce the yield drastically in some areas. The major breeding objectives for wheat include grain yield, earliness, resistance to lodging and diseases, spikelet fertility, cold tolerance, leaf duration and net assimilation rate, fertilizer utilization, coleoptile length, nutritional value, organoleptic qualities, and the improvement of charac­ ters such as color and milling yield. The breeding of wheat by traditional methods has been practiced for centuries, however, it has only now come to a stage where these methods are insufficient to make any further breakthrough or to cope with the world's demand. Although numerous varieties are released every year around the world, they do not last long, and long-term objectives cannot be realized unless more genetic variability is generated. Moreover, the intro­ duction of exotic genetic stocks and their cultivation over large areas results in the depletion and loss of the native germplasm pool.

Multiscale Modeling (Biotechnology in Agriculture & Forestry)

Science & Math

Ferreira, Marco A.R.

Lee, Herbert K.H.

Biological Sciences

Springer

<p>This highly useful book contains methodology for the analysis of data that arise from multiscale processes. It brings together a number of recent developments and makes them accessible to a wider audience. Taking a Bayesian approach allows for full accounting of uncertainty, and also addresses the delicate issue of uncertainty at multiple scales. These methods can handle different amounts of prior knowledge at different scales, as often occurs in practice.</p>

Multiscale Modeling: A Bayesian Perspective (Springer Series in Statistics)

Business & Money

Economics

<p>Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. It discusses neural networks in a statistical context, an approach in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and ways to deal with this issue, exploring ideas from statistics and machine learning. An analysis on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, this book will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.</p>

Bayesian Nonparametrics via Neural Networks (ASA-SIAM Series on Statistics and Applied Probability, Series Number 13)

Computers & Technology

Certification

SIAM: Society for Industrial and Applied Mathematics

This highly useful book contains methodology for the analysis of data that arise from multiscale processes. It brings together a number of recent developments and makes them accessible to a wider audience. Taking a Bayesian approach allows for full accounting of uncertainty, and also addresses the delicate issue of uncertainty at multiple scales. These methods can handle different amounts of prior knowledge at different scales, as often occurs in practice.

Ferreira, Marco A.R. A.R.

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