{"title":"Books by Committee on Applied and Theoretical Statistics","description":null,"products":[{"product_id":"uncertainty-management-in-remote-sensing-of-climate-data","title":"Uncertainty Management in Remote Sensing of Climate Data","description":"Great advances have been made in our understanding of the climate system over the past few decades, and remotely sensed data have played a key role in supporting many of these advances. Improvements in satellites and in computational and data-handling techniques have yielded high quality, readily accessible data. However, rapid increases in data volume have also led to large and complex datasets that pose significant challenges in data analysis. Uncertainty characterization is needed for every satellite mission and scientists continue to be challenged by the need to reduce the uncertainty in remotely sensed climate records and projections. The approaches currently used to quantify the uncertainty in remotely sensed data lack an overall mathematically based framework. An additional challenge is characterizing uncertainty in ways that are useful to a broad spectrum of end-users.\n\n\u003cbr\u003e\u003cbr\u003e\n\nIn December 2008, the National Academies held a workshop, summarized in this volume, to survey how statisticians, climate scientists, and remote sensing experts might address the challenges of uncertainty management in remote sensing of climate data. The workshop emphasized raising and discussing issues that could be studied more intently by individual researchers or teams of researchers, and setting the stage for possible future collaborative activities.","brand":"Gardners","offers":[{"title":"Default Title","offer_id":57466532069749,"sku":"9780309139588","price":41.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0612\/7193\/3106\/files\/9780309139588.jpg?v=1785654589"},{"product_id":"refining-the-concept-of-scientific-inference-when-working-with-big-data","title":"Refining the Concept of Scientific Inference When Working with Big Data","description":"\u003cp\u003eThe concept of utilizing big data to enable scientific discovery has generated tremendous excitement and investment from both private and public sectors over the past decade, and expectations continue to grow. Using big data analytics to identify complex patterns hidden inside volumes of data that have never been combined could accelerate the rate of scientific discovery and lead to the development of beneficial technologies and products. However, producing actionable scientific knowledge from such large, complex data sets requires statistical models that produce reliable inferences (NRC, 2013). Without careful consideration of the suitability of both available data and the statistical models applied, analysis of big data may result in misleading correlations and false discoveries, which can potentially undermine confidence in scientific research if the results are not reproducible. In June 2016 the National Academies of Sciences, Engineering, and Medicine convened a workshop to examine critical challenges and opportunities in performing scientific inference reliably when working with big data. Participants explored new methodologic developments that hold significant promise and potential research program areas for the future. This publication summarizes the presentations and discussions from the workshop.\u003c\/p\u003e","brand":"Gardners","offers":[{"title":"Default Title","offer_id":57466641318261,"sku":"9780309454445","price":38.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0612\/7193\/3106\/files\/9780309454445.jpg?v=1785654699"},{"product_id":"foundational-research-gaps-and-future-directions-for-digital-twins","title":"Foundational Research Gaps and Future Directions for Digital Twins","description":"\u003cp\u003eAcross multiple domains of science, engineering, and medicine, excitement is growing about the potential of digital twins to transform scientific research, industrial practices, and many aspects of daily life. A digital twin couples computational models with a physical counterpart to create a system that is dynamically updated through bidirectional data flows as conditions change. Going beyond traditional simulation and modeling, digital twins could enable improved medical decision-making at the individual patient level, predictions of future weather and climate conditions over longer timescales, and safer, more efficient engineering processes. However, many challenges remain before these applications can be realized.\u003c\/p\u003e\n\n\u003cp\u003eThis report identifies the foundational research and resources needed to support the development of digital twin technologies.  The report presents critical future research priorities and an interdisciplinary research agenda for the field, including how federal agencies and researchers across domains can best collaborate.\u003c\/p\u003e","brand":"Gardners","offers":[{"title":"Default Title","offer_id":57466676609397,"sku":"9780309700429","price":27.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0612\/7193\/3106\/files\/9780309700429.jpg?v=1785654736"},{"product_id":"massive-data-sets","title":"Massive Data Sets","description":null,"brand":"Gardners","offers":[{"title":"Default Title","offer_id":57466684277109,"sku":"9780309056946","price":125.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0612\/7193\/3106\/files\/9780309056946.jpg?v=1785654743"},{"product_id":"discriminant-analysis-and-clustering","title":"Discriminant Analysis and Clustering","description":null,"brand":"Gardners","offers":[{"title":"Default Title","offer_id":57466756432245,"sku":"9780309090384","price":148.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0612\/7193\/3106\/files\/9780309090384.jpg?v=1785654817"}],"url":"https:\/\/backstory.london\/collections\/author-committee-on-applied-and-theoretical-statistics.oembed","provider":"Backstory","version":"1.0","type":"link"}