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Growth Disorders --- Anthropometry. --- Models, Statistical. --- Body Weight --- Body Mass Index. --- Body Height --- Puberty --- Quetelet's Index --- Quetelet Index --- Index, Body Mass --- Index, Quetelet --- Quetelets Index --- Model, Statistical --- Models, Binomial --- Models, Polynomial --- Statistical Model --- Probabilistic Models --- Statistical Models --- Two-Parameter Models --- Binomial Model --- Binomial Models --- Model, Binomial --- Model, Polynomial --- Model, Probabilistic --- Model, Two-Parameter --- Models, Probabilistic --- Models, Two-Parameter --- Polynomial Model --- Polynomial Models --- Probabilistic Model --- Two Parameter Models --- Two-Parameter Model --- Statistics as Topic --- Anthropology, Physical --- Body Composition --- Body Weights and Measures --- diagnosis. --- ethnology. --- Theses --- Anthropometry --- Models, Statistical --- Body Mass Index --- diagnosis --- ethnology

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Mathematical statistics --- Data Interpretation, Statistical --- Models, Statistical --- Probabilities --- Statistique mathématique --- Probabilités --- Periodicals --- Périodiques --- Mathematical statistics. --- Mathematical Sciences --- Applied Mathematics --- General and Others --- Numerical Analysis --- Statistics --- gegevensanalyse --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Sampling (Statistics) --- Statistical methods --- Model, Statistical --- Models, Binomial --- Models, Polynomial --- Statistical Model --- Probabilistic Models --- Statistical Models --- Two-Parameter Models --- Binomial Model --- Binomial Models --- Model, Binomial --- Model, Polynomial --- Model, Probabilistic --- Model, Two-Parameter --- Models, Probabilistic --- Models, Two-Parameter --- Polynomial Model --- Polynomial Models --- Probabilistic Model --- Two Parameter Models --- Two-Parameter Model --- Statistics as Topic --- Data Interpretations, Statistical --- Interpretation, Statistical Data --- Statistical Data Analysis --- Statistical Data Interpretation --- Data Analysis, Statistical --- Analyses, Statistical Data --- Analysis, Statistical Data --- Data Analyses, Statistical --- Interpretations, Statistical Data --- Statistical Data Analyses --- Statistical Data Interpretations

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"The vast array of molecular level information now available presents exciting opportunities to characterize the genetic underpinnings of complex diseases while discovering novel biological pathways to disease progression. In this introductory graduate level text, Dr. Foulkes elucidates core concepts that undergird the wide range of analytic techniques and software tools for the analysis of data derived from population-based genetic investigations. Applied Statistical Genetics with R offers a clear and cogent presentation of several fundamental statistical approaches that researchers from multiple disciplines, including medicine, public health, epidemiology, statistics and computer science, will find useful in exploring this emerging field. Couched in the language of biostatistics, this text can be easily adopted for public health and medical school curricula. The text covers key genetic data concepts and statistical principles to provide the reader with a strong foundation in methods for candidate gene and genome-wide association studies. These include methods for unobservable haplotypic phase, multiple testing adjustments, and high-dimensional data analysis. Emphasis is on analysis of data arising from studies of unrelated individuals and the potential interplay among genetic factors and more traditional, epidemiological risk factors for disease. While theoretically rigorous, the analytic techniques are presented at a level that will appeal to researchers and students with limited knowledge of statistical genetics. The text assumes the reader has completed a first course in biostatistics, uses publicly available data sets for illustration, and provides extensive examples using the open source, publicly available statistical software environment R."--Publisher's website.

Genetics --- R (Computer program language) --- Genetics, Population --- Epidemiologic Methods --- Automatic Data Processing --- Models, Statistical --- Statistical methods --- methods --- Electronic Data Processing --- GNU-S (Computer program language) --- Domain-specific programming languages --- Model, Statistical --- Models, Binomial --- Models, Polynomial --- Statistical Model --- Probabilistic Models --- Statistical Models --- Two-Parameter Models --- Binomial Model --- Binomial Models --- Model, Binomial --- Model, Polynomial --- Model, Probabilistic --- Model, Two-Parameter --- Models, Probabilistic --- Models, Two-Parameter --- Polynomial Model --- Polynomial Models --- Probabilistic Model --- Two Parameter Models --- Two-Parameter Model --- Statistics as Topic --- Information Processing --- Bar Codes --- Computer Data Processing --- Data Processing, Automatic --- Information Processing, Automatic --- Optical Readers --- Automatic Information Processing --- Bar Code --- Codes, Bar --- Data Processing, Computer --- Data Processing, Electronic --- Optical Reader --- Processing, Automatic Data --- Processing, Automatic Information --- Processing, Computer Data --- Processing, Electronic Data --- Processing, Information --- Computers --- Epidemiologic Method --- Epidemiological Methods --- Methods, Epidemiologic --- Epidemiological Method --- Method, Epidemiologic --- Method, Epidemiological --- Methods, Epidemiological --- Epidemiology --- Genetics - Statistical methods --- Genetics, Population - methods

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"This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers."--Jacket

Biomathematics. Biometry. Biostatistics --- Animal genetics. Animal evolution --- medische statistiek --- bio-informatica --- biostatistiek --- genetica --- biometrie --- Bioinformatics --- R (Computer program language) --- Computational biology --- Programming languages (Electronic computers) --- Computational Biology --- Models, Statistical --- Programming Languages --- Language, Programming --- Languages, Programming --- Programming Language --- Model, Statistical --- Models, Binomial --- Models, Polynomial --- Statistical Model --- Probabilistic Models --- Statistical Models --- Two-Parameter Models --- Binomial Model --- Binomial Models --- Model, Binomial --- Model, Polynomial --- Model, Probabilistic --- Model, Two-Parameter --- Models, Probabilistic --- Models, Two-Parameter --- Polynomial Model --- Polynomial Models --- Probabilistic Model --- Two Parameter Models --- Two-Parameter Model --- Statistics as Topic --- Biology --- Computer languages --- Computer program languages --- Computer programming languages --- Machine language --- Electronic data processing --- Languages, Artificial --- GNU-S (Computer program language) --- Domain-specific programming languages --- Bio-informatics --- Biological informatics --- Information science --- Systems biology --- methods --- Data processing --- Bioconductor (Computer file) --- Bio-Informatics --- Biology, Computational --- Computational Molecular Biology --- Molecular Biology, Computational --- Bio Informatics --- Bio-Informatic --- Bioinformatic --- Biologies, Computational Molecular --- Biology, Computational Molecular --- Computational Molecular Biologies --- Molecular Biologies, Computational --- Computational Chemistry --- Genomics

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The book focuses on the techniques for UAV-based 3D mapping and its applications in varying fields since the explosive development of UAV-based photogrammetric 3D mapping and their wide applications from traditional surveying and mapping to other related fields have been witnessed in photogrammetry and remote sensing. In the last decade, unmanned aerial vehicle (UAV) images have become one of the most important remote sensing data sources for photogrammetric 3D mapping. Besides, the rapid development of recent techniques, e.g., SfM (Structure from Motion) for off-line image orientation, SLAM (Simultaneous Localization and Mapping) for on-line UAV navigation, and the deep learning (DL) embedded 3D reconstruction pipeline, has promoted UAV-based 3D mapping towards the direction of automation and intelligence. It is really worthy to collecting the cutting-edge techniques and reporting their promising applications.

Technology: general issues --- History of engineering & technology --- compound building reconstruction --- LiDAR --- point clouds --- semantic decomposition --- structure from motion --- match pair --- cycle consistency inference --- repetitive structure --- very short baseline --- high-resolution remote sensing images --- building extraction --- multiscale features --- aggregate semantic information --- feature pyramid --- spatial eight-quadrant kernel convolution --- 3D point cloud --- semantic segmentation --- indoor scene --- wide-baseline stereo image --- deep learning --- convolutional neural network --- affine invariant feature --- image matching --- photogrammetric mesh model --- building façade --- 3D reconstruction --- least square fitting --- single image super-resolution --- lightweight image super-resolution --- U-shaped residual network --- dense shortcut --- effective feature distillation --- high-frequency loss --- power lines --- UAV inspection --- red-black propagation --- depth map fusion --- PatchMatch --- digital photogrammetry --- camera self-calibration --- Brown model --- polynomial model --- aerial triangulation --- GF-7 image --- building footprint --- building height --- multi-view --- point cloud --- multi-view reconstruction --- detail preserving --- depth estimation --- surface meshing --- texture mapping --- coplanar extraction --- deep convolutional neural network --- geometric topology --- homography matrix --- airborne LiDAR --- coal mine --- surface subsidence --- deformation detection --- digital subsidence model --- compound building reconstruction --- LiDAR --- point clouds --- semantic decomposition --- structure from motion --- match pair --- cycle consistency inference --- repetitive structure --- very short baseline --- high-resolution remote sensing images --- building extraction --- multiscale features --- aggregate semantic information --- feature pyramid --- spatial eight-quadrant kernel convolution --- 3D point cloud --- semantic segmentation --- indoor scene --- wide-baseline stereo image --- deep learning --- convolutional neural network --- affine invariant feature --- image matching --- photogrammetric mesh model --- building façade --- 3D reconstruction --- least square fitting --- single image super-resolution --- lightweight image super-resolution --- U-shaped residual network --- dense shortcut --- effective feature distillation --- high-frequency loss --- power lines --- UAV inspection --- red-black propagation --- depth map fusion --- PatchMatch --- digital photogrammetry --- camera self-calibration --- Brown model --- polynomial model --- aerial triangulation --- GF-7 image --- building footprint --- building height --- multi-view --- point cloud --- multi-view reconstruction --- detail preserving --- depth estimation --- surface meshing --- texture mapping --- coplanar extraction --- deep convolutional neural network --- geometric topology --- homography matrix --- airborne LiDAR --- coal mine --- surface subsidence --- deformation detection --- digital subsidence model