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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Science: general issues --- Psychology --- epistemology --- methodology --- data science --- implementation science --- Bayesian approach --- replicability

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This book provides a comprehensive review of novel adaptive trial designs for targeted therapies and immunotherapies. This book covers a wide range of novel statistical designs for various clinical settings, including early phase dose-escalation study, proof-of-concept trials, and confirmatory studies with registrational. The book includes real-life examples and software to facilitate practitioners to learn and use the designs in practice.

Biometry. --- Biostatistics. --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Biomathematics --- Statistics --- Statistical methods --- Clinical Trial Protocols as Topic. --- Adaptive Clinical Trials as Topic. --- Bayes Theorem. --- Analysis, Bayesian --- Bayesian Approach --- Bayesian Analysis --- Bayesian Forecast --- Bayesian Method --- Bayesian Prediction --- Approach, Bayesian --- Approachs, Bayesian --- Bayesian Approachs --- Forecast, Bayesian --- Method, Bayesian --- Prediction, Bayesian --- Theorem, Bayes --- Anthropology, Physical --- Statistics as Topic --- Biological Statistics --- Biological Statistic --- Statistic, Biological --- Statistics, Biological --- Adaptive Clinical Trial Design --- Adaptive Clinical Trial as Topic --- Immunoteràpia --- Dianes farmacològiques --- Assaigs clínics --- Estadística matemàtica --- Estadística bayesiana --- Bayesian statistical decision theory.

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This Special Issue of Atmosphere focuses on hydrometeorological extremes and their local impacts on human–environment systems. Particularly, we accepted submissions on the topics of observational and model-based studies that could provide useful information for infrastructure design, decision making, and policy making to achieve our goals of enhancing the resilience of human–environment systems to climate change and increased variability.

flood risk --- urban flood forecasting and warning --- inland-river combined flood system --- LSTM --- artificial neural network --- neurons --- layers --- temperature --- South Korea --- deep learning --- reference evapotranspiration --- climate change --- drought --- meteorological extremes --- climatic variables --- wind speed --- extreme El Niño event --- tropical cyclone --- tropical cyclone-induced precipitation --- China --- Bayesian approach --- nonstationarity --- reanalysis products --- quantile delta mapping --- ranges of flood sizes --- specific flood distributions --- ungauged watersheds --- influence of rainfall characteristics --- depth-averaged temperature --- decision tree --- lifetime maximum intensity --- climate variability --- seasonality --- dengue fever --- vector --- rainfall --- Bangladesh --- copula function --- drought duration --- drought severity --- land-ocean temperature contrast/meridional temperature gradient --- standardized precipitation evapotranspiration index

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"Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online"-- "Vast amounts of data present amajor challenge to all thoseworking in computer science, and its many related fields, who need to process and extract value from such data. Machine learning technology is already used to help with this task in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis and robot locomotion. As its usage becomes more widespread, no student should be without the skills taught in this book. Designed for final-year undergraduate and graduate students, this gentle introduction is ideally suited to readers without a solid background in linear algebra and calculus. It covers everything from basic reasoning to advanced techniques in machine learning, and rucially enables students to construct their own models for real-world problems by teaching them what lies behind the methods. Numerous examples and exercises are included in the text. Comprehensive resources for students and instructors are available online"--

519.22 --- 519.22 Statistical theory. Statistical models. Mathematical statistics in general --- Statistical theory. Statistical models. Mathematical statistics in general --- Machine learning. --- Bayesian statistical decision theory. --- Computers --- Computer Vision & Pattern Recognition. --- Bayesian statistical decision theory --- Machine learning --- Learning, Machine --- Artificial intelligence --- Machine theory --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Machine Learning --- COMPUTERS / Computer Vision & Pattern Recognition. --- Apprentissage automatique --- Statistique bayésienne --- Artificial Intelligence --- Bayes Theorem --- Analysis, Bayesian --- Bayesian Approach --- Bayesian Analysis --- Bayesian Forecast --- Bayesian Method --- Bayesian Prediction --- Approach, Bayesian --- Approachs, Bayesian --- Bayesian Approachs --- Forecast, Bayesian --- Method, Bayesian --- Prediction, Bayesian --- Theorem, Bayes --- Computational Intelligence --- AI (Artificial Intelligence) --- Computer Reasoning --- Computer Vision Systems --- Knowledge Acquisition (Computer) --- Knowledge Representation (Computer) --- Machine Intelligence --- Acquisition, Knowledge (Computer) --- Computer Vision System --- Intelligence, Artificial --- Intelligence, Computational --- Intelligence, Machine --- Knowledge Representations (Computer) --- Reasoning, Computer --- Representation, Knowledge (Computer) --- System, Computer Vision --- Systems, Computer Vision --- Vision System, Computer --- Vision Systems, Computer --- Heuristics --- 681.3*I2 --- 681.3*I2 Artificial intelligence. AI --- Artificial intelligence. AI

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Meta-analysis is a powerful statistical methodology for synthesizing research evidence across independent studies. This is the first comprehensive handbook of meta-analysis written specifically for ecologists and evolutionary biologists, and it provides an invaluable introduction for beginners as well as an up-to-date guide for experienced meta-analysts. The chapters, written by renowned experts, walk readers through every step of meta-analysis, from problem formulation to the presentation of the results. The handbook identifies both the advantages of using meta-analysis for research synthesis and the potential pitfalls and limitations of meta-analysis (including when it should not be used). Different approaches to carrying out a meta-analysis are described, and include moment and least-square, maximum likelihood, and Bayesian approaches, all illustrated using worked examples based on real biological datasets. This one-of-a-kind resource is uniquely tailored to the biological sciences, and will provide an invaluable text for practitioners from graduate students and senior scientists to policymakers in conservation and environmental management. Walks you through every step of carrying out a meta-analysis in ecology and evolutionary biology, from problem formulation to result presentation Brings together experts from a broad range of fields Shows how to avoid, minimize, or resolve pitfalls such as missing data, publication bias, varying data quality, nonindependence of observations, and phylogenetic dependencies among species Helps you choose the right software Draws on numerous examples based on real biological datasets

Meta-analysis. --- Evolution --- Ecology --- Philosophy --- Creation --- Emergence (Philosophy) --- Teleology --- Medicine --- Psychometrics --- Social sciences --- Mathematical models. --- Statistical methods. --- Research --- Evaluation --- Statistical methods --- Bayesian analysis. --- Bayesian approach. --- Lepidoptera mating. --- allometric scaling. --- average trends. --- biodiversity. --- collaborative research. --- computer software. --- conceptual tool. --- conservation. --- conventional wisdom. --- data analysis. --- data appraisal. --- data collection. --- data extraction. --- data gathering. --- data quality. --- ecology. --- effect size. --- effect sizes. --- evolution. --- evolutionary biology. --- exemplar studies. --- forest plots. --- imputation methods. --- insufficient data. --- interaction effects. --- invasive plants. --- knowledge gaps. --- large-scale monitoring. --- least-squares method. --- literature search. --- management intervention. --- maximum likelihood estimation. --- medicine. --- meta-analysis database. --- meta-analysis. --- meta-analytic process. --- meta-regression plots. --- missing data. --- moment-based approach. --- non-independence. --- parameter estimation. --- partial information. --- phylogenetic nonindependence. --- phylogenetic relationships. --- plant abundance. --- primary data. --- problem formulation. --- publication bias. --- published studies. --- quantitative research synthesis. --- research evidence. --- research pooling. --- research practice. --- research synthesis. --- sample error. --- sampling. --- scatter plots. --- scientific literature. --- scientific publications. --- scoping search. --- sex ratio theory. --- sexual selection. --- small-scale surveys. --- social sciences. --- statistical analysis. --- statistical inference. --- statistical methodology. --- statistical models. --- statistical power. --- statistical software. --- statistical tool. --- study quality. --- study replication. --- study selection. --- subjectivity. --- systematic research synthesis. --- systematic review. --- systematic reviews. --- systematic search. --- temporal change. --- temporal trends. --- visualization.