Exploring Mathematical Modeling in Biology through Case Studies and Experimental Activities provides supporting materials for courses taken by students majoring in mathematics, computer science or in the life sciences. The book's cases and lab exercises focus on hypothesis testing and model development in the context of real data. The supporting mathematical, coding and biological background permit readers to explore a problem, understand assumptions, and the meaning of their results. The experiential components provide hands-on learning both in the lab and on the computer. As a beginning text in modeling, readers will learn to value the approach and apply competencies in other settings.
Included case studies focus on building a model to solve a particular biological problem from concept and translation into a mathematical form, to validating the parameters, testing the quality of the model and finally interpreting the outcome in biological terms. The book also shows how particular mathematical approaches are adapted to a variety of problems at multiple biological scales. Finally, the labs bring the biological problems and the practical issues of collecting data to actually test the model and/or adapting the mathematics to the data that can be collected.
Presents a single volume on mathematics and biological examples, with data and wet lab experiences suitable for non-experts
Contains three real-world biological case studies and one wet lab for application of the mathematical models
Includes R code templates throughout the text, which are also available through an online repository, along with the necessary data files to complete all projects and labs
Описание: This authored monograph supplies empirical evidence for the Bayesian brain hypothesis by modeling event-related potentials (ERP) of the human electroencephalogram (EEG) during successive trials in cognitive tasks. Bayesian model selection is then used to choose the model which best explains the ERP amplitude fluctuations.
Название: Introductory Mathematics and Statistics through Sports ISBN: 0198835671 ISBN-13(EAN): 9780198835677 Издательство: Oxford Academ Рейтинг: Цена: 6101.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introductory Mathematics and Statistics through Sports uses sport as a tool to help students get to grips with mathematics and statistics, placing great emphasis on communication, application, and internalization of mathematics.
Описание: A Toolbox for Digital Twins: From Model-Based to Data-Driven brings together the mathematical and numerical frameworks needed for developing digital twins (DTs). Starting from the basics—probability, statistics, numerical methods, optimization, and machine learning—and moving on to data assimilation, inverse problems, and Bayesian uncertainty quantification, the book provides a comprehensive toolbox for DTs. Readers will findguidelines and decision trees to help the reader choose the right tools for the job,emphasis on the design process, denoted as the "inference cycle," whose aim is to propose a global methodology for complex problems,a comprehensive reference section with all recent methods, covering both model-based and data-driven approaches, anda vast selection of examples and all accompanying code. A Toolbox for Digital Twins: From Model-Based to Data-Driven is for researchers and engineers, engineering students, and scientists in any domain where data and models need to be coupled to produce digital twins.
Описание: Translating Diverse Environmental Data into Reliable Information: How to Coordinate Evidence from Different Sources is a resource for building environmental knowledge, particularly in the era of Big Data. Environmental scientists, engineers, educators and students will find it essential to determine data needs, assess their quality, and efficiently manage their findings. Decision makers can explore new open access databases and tools, especially portals and dashboards. The book demonstrates how environmental knowledgebases are and can be built to meet the needs of modern students and professionals. Topics covered include concepts and principles that underpin air, water, and other public health and ecological topics. Integrated and systems perspectives are woven throughout, with clues on how to build and apply interdisciplinary data, which can increasingly be obtained from sources ranging from peer-reviewed research appearing in scientific journals to information gathered by citizen scientists. This opens the door to using vast amounts of open data and the necessary quality assurance and metadata considerations for their countless applications.
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