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Biomathematics --- Behavior genetics

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From controlling disease outbreaks to predicting heart attacks, dynamic models are increasingly crucial for understanding biological processes. Many universities are starting undergraduate programs in computational biology to introduce students to this rapidly growing field. In Dynamic Models in Biology, the first text on dynamic models specifically written for undergraduate students in the biological sciences, ecologist Stephen Ellner and mathematician John Guckenheimer teach students how to understand, build, and use dynamic models in biology. Developed from a course taught by Ellner and Guckenheimer at Cornell University, the book is organized around biological applications, with mathematics and computing developed through case studies at the molecular, cellular, and population levels. The authors cover both simple analytic models--the sort usually found in mathematical biology texts--and the complex computational models now used by both biologists and mathematicians. Linked to a Web site with computer-lab materials and exercises, Dynamic Models in Biology is a major new introduction to dynamic models for students in the biological sciences, mathematics, and engineering.

Biology --- Mathematical models. --- Biological models --- Biomathematics

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Although there have been significant developments in the field of biometrics in recent years, much of the research in this area has focused upon the front-end technology. This unique Guide to Biometrics for Large-Scale Systems considers biometric technology in a broader light, integrating the concept more seamlessly into mainstream Information Technology, while also striving to understand the cultural attitudes and the societal impact of identity management. This approach represents a step-change in current thinking - that may come to be viewed as a milestone in the development of biometric technology - in which many established tenets are placed under considerable scrutiny. Topics and features: Summarizes the material covered at the beginning of every chapter, and provides chapter-ending review questions and discussion points Reviews identity verification in nature, and early historical interest in anatomical measurement, which form the foundations of the modern field of biometrics Provides an overview of biometric technology, presents a focus on biometric systems and true systems integration, examines the concept of identity management, and predicts future trends Investigates performance issues in biometric systems, the management and security of biometric data, and the impact of mobile devices on biometrics technology Explains the equivalence of performance across operational nodes and why this is so important, introducing the APEX system as an example of how this issue can be addressed Considers the legal, political and societal factors of biometric technology, in addition to user psychology and other human factors This highly practical reference/guidebook is an invaluable resource for program managers, application developers and consultants working in this area. Students interested in biometric technology will also find this a must-read. Julian Ashbourn is an experienced and successful author on topics ranging from computer science and identity management through to geoscience and the natural sciences in general. His publications include the Springer titles Practical Biometrics: From Aspiration to Implementation, BANTAM User Guide: Biometric and Token Technology Application Modeling Language and Geological Landscapes of Britain.

Biomathematics. Biometry. Biostatistics --- biomathematica --- biostatistiek --- biometrie

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"The second edition of a bestseller, this textbook delineates stochastic processes, emphasizing applications in biology. It includes MATLAB throughout the book to help with the solutions of various problems. The book is organized according to the three types of stochastic processes: discrete time Markov chains, continuous time Markov chains and continuous time and state Markov processes. It contains a new chapter on the biological applications of stochastic differential equations and new sections on alternative methods for derivation of a stochastic differential equation, data and parameter estimation, Monte Carlo simulation, and more"--

Stochastic processes --- Biomathematics --- Processus stochastiques --- Biomathématiques --- Stochastic processes. --- Biomathematics. --- Biomathématiques

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Calculus. --- Calculus --- Social sciences --- Biomathematics. --- Mathematics. --- Mathematical analysis