Crc press feb 2026 (4 risultati)

Buch. Condizione: Neu. Neuware - Biology at all scales has become a data-driven science, with large-scale datasets driving fields from population genomics to ecology. Practicing biologists have no choice but to use computational approaches, statistics, modeling, and other data science tools in their research. However, undergraduate biology education still primarily focuses on nonquantitative descriptions. This book provides students whose background is in biology with an introduction to modeling biological systems using mathematical, computational, and statistical tools. It is based on a series of hands-on analyses conducted with open-source tools that allow the students to discover for themselves emergent properties of biological systems that are not evident without using model-based approaches. The goal of this book is to provide a 'turn-key' introductory quantitative biology course suitable for all biology students. The book provides the narrative for the analyses and discussions to be done in class, with support from the included website, slides, and test material.Key Features - Written in an accessible, narrative style - Includes hands-on analyses with open-source tools - Integrates biology across spatial and temporal scales - Links to a course website with interactive tools - Brings biological education into the 'data science' era - Each chapter includes a variety of exercises designed to actively engage the reader - Lecture slides and animations to cover the key arguments and derivations in each chapter, as well as example exam questions, are available for qualified instructors.…

Buch. Condizione: Neu. Neuware - A Cultural History of Computer Graphics presents a fundamentally new approach to analyzing digital images aesthetically through the example of 3D computer graphics (CG). While numerous methods for creating digital imagery have long existed, the advent of AI-generated content is causing a rise in debates and conflict. It is becoming increasingly difficult to aesthetically differentiate digital photographs, CG and AI images, and yet, because these types of images carry different cultural or even political implications, it is becoming increasingly important to do so. In response to the need of new methods to culturally decode digital imagery, this book starts from the production process and describes computer graphics as an independent method of expression, containing a specific ideological concept of realism. Through this study, it becomes clear that a particular understanding of the world is inscribed in computer graphics software and, consequently, the image creation process. As the image surface does not reveal much about these cultural artifacts, it becomes necessary to focuson the historical development of this imaging practice and analyze it production-aesthetically. In its own unique way, this is true for every digital imaging method. Each embodies its own sense of the world that is only accessible through their production aesthetics. This book will be of great interest to researchers of computer graphics, 3D image generation and the cultural history of computer-generated imagery.…

Buch. Condizione: Neu. Neuware - Machine learning fundamentally learns from the past experiences (seen data) to make predictions about future (unseen data). Predictions in nature are often uncertain. Microbiome data have unique characteristics, including high-dimensionality, over-dispersion, sparsity and zero-inflation, and heterogeneity. Thus, machine learning involving microbiome data for predicting the outcome of phenotypes is even more uncertain than learning those data from other fields. Machine Learning for Microbiome Statistics poses many challenges for evaluating the prediction performance using appropriate metrics and independent data validation.This unique book aims to address the challenges of machine learning statistics, emphasize the importance of performance valuation by appropriate metrics and independent data, and describe several important concepts of machine learning statistics, such as feature engineering and overfitting. It comprehensively reviews commonly used and newly developed machine learning models for microbiome research. Specifically, this book provides the step-by-step procedures to perform machine learning of microbiome data, including feature engineering, algorithm selection and optimization, performance evaluation and model testing. It comments the benefits and limitations of using machine learning for microbiome statistics and remarks on the advantages and disadvantages of each machine learning algorithm.It will be an excellent reference book for students and academics in the field. - Presents a thorough overview of machine learning algorithms for microbiome statistics. - Performs step-by-step procedures to perform machine learning of microbiome data, using important supervised learning algorithms, including classical, ensemble learning and tree-based models. - Describes important concepts of machine learning, including bias and variance tradeoff, accuracy and precision, overfitting and underfitting, model complexity and interpretability, and feature engineering. - Investigates and applies various cross-validation techniques step-by-step. - Introduces confusion matrix and its derived measures. Comprehensively describes the properties of F1, Matthews' correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), and area under the precision-recall curve (AUC-PR), as well as discusses their advantages and disadvantages when using them for microbiome data. - Offers all related R codes and the datasets from the authors' first-hand microbiome research and publicly available data. …

Buch. Condizione: Neu. Neuware - The goals of this new, second edition of this book are to develop the skills and an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing dependent data. An expanded feature of this edition is the inclusion of many nontrivial data sets illustrating the wealth of potential applications to problems in the biological, physical, and social sciences as well as in economics and medicine.This edition emphasizes a variety of methodological techniques to illustrate solutions to data analysis problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and the analysis of economic and financial problems.Key Features:¿ Presents a balanced and comprehensive treatment of both time and frequency domain methods with an emphasis on data analysis.¿ Detailed R code is included with each numerical example.¿ Includes nontrivial data sets.The book can be used for a one semester/quarter introductory time series course where the prerequisites are an understanding of linear regression, basic calculus-based probability and statistics skills, and math skills at the high-school level. All the numerical examples use the R statistical package without assuming the reader has previously used the software.…