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Author: Peter Quell
Publisher:
ISBN: 9781782722632
Category : Risk management
Languages : en
Pages :
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Book Description
Author: Peter Quell
Publisher:
ISBN: 9781782722632
Category : Risk management
Languages : en
Pages :
Get Book Here
Book Description
Author: William L. Oberkampf
Publisher: Cambridge University Press
ISBN: 1139491768
Category : Computers
Languages : en
Pages : 782
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Book Description
Advances in scientific computing have made modelling and simulation an important part of the decision-making process in engineering, science, and public policy. This book provides a comprehensive and systematic development of the basic concepts, principles, and procedures for verification and validation of models and simulations. The emphasis is placed on models that are described by partial differential and integral equations and the simulations that result from their numerical solution. The methods described can be applied to a wide range of technical fields, from the physical sciences, engineering and technology and industry, through to environmental regulations and safety, product and plant safety, financial investing, and governmental regulations. This book will be genuinely welcomed by researchers, practitioners, and decision makers in a broad range of fields, who seek to improve the credibility and reliability of simulation results. It will also be appropriate either for university courses or for independent study.
Author: George A. Christodoulakis
Publisher: Elsevier
ISBN: 0080553885
Category : Business & Economics
Languages : en
Pages : 217
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Book Description
Risk model validation is an emerging and important area of research, and has arisen because of Basel I and II. These regulatory initiatives require trading institutions and lending institutions to compute their reserve capital in a highly analytic way, based on the use of internal risk models. It is part of the regulatory structure that these risk models be validated both internally and externally, and there is a great shortage of information as to best practise. Editors Christodoulakis and Satchell collect papers that are beginning to appear by regulators, consultants, and academics, to provide the first collection that focuses on the quantitative side of model validation. The book covers the three main areas of risk: Credit Risk and Market and Operational Risk.*Risk model validation is a requirement of Basel I and II *The first collection of papers in this new and developing area of research *International authors cover model validation in credit, market, and operational risk
Author: David Wanetick
Publisher:
ISBN: 9780692369562
Category :
Languages : en
Pages : 376
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Book Description
Over twenty years of high-level analytical experience-including hundreds of CEO interviews-are unleashed on the pages of Business Model Validation. Hundreds of valuable insights-regarding industries as diverse as textbook publishers to online pornography purveyors and from cement producers to death care operators-were selected to enable readers to maximize their returns-on-investment. David Wanetick reveals his groundbreaking analysis into emerging business models such as those of on-demand taxis, home sharing, Bitcoin, music streaming, drones, crowdfunding, marijuana dispensaries, electronic cigarettes, flash sales operators, freemium businesses, electric vehicles, massive open online course operators (MOOCs), cloud storage and 3-D printers.
Author: Hannes Hapke
Publisher: "O'Reilly Media, Inc."
ISBN: 1492053147
Category : Computers
Languages : en
Pages : 358
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Book Description
Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques
Author: S. Scandizzo
Publisher: Springer
ISBN: 1137436964
Category : Business & Economics
Languages : en
Pages : 242
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Book Description
This book is a one-stop-shop reference for risk management practitioners involved in the validation of risk models. It is a comprehensive manual about the tools, techniques and processes to be followed, focused on all the models that are relevant in the capital requirements and supervisory review of large international banks.
Author: Zhu Mao
Publisher: Springer Nature
ISBN: 3030476383
Category : Technology & Engineering
Languages : en
Pages : 426
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Book Description
Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics, 2020, the third volume of nine from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on: Uncertainty Quantification in Material Models Uncertainty Propagation in Structural Dynamics Practical Applications of MVUQ Advances in Model Validation & Uncertainty Quantification: Model Updating Model Validation & Uncertainty Quantification: Industrial Applications Controlling Uncertainty Uncertainty in Early Stage Design Modeling of Musical Instruments Overview of Model Validation and Uncertainty
Author: Todd Simmermacher
Publisher: Springer Science & Business Media
ISBN: 1461465648
Category : Technology & Engineering
Languages : en
Pages : 264
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Book Description
Topics in Model Validation and Uncertainty Quantification, Volume : Proceedings of the 31st IMAC, A Conference and Exposition on Structural Dynamics, 2013, the fifth volume of seven from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Dynamics, including papers on: Uncertainty Quantification & Propagation in Structural Dynamics Robustness to Lack of Knowledge in Design Model Validation
Author: Malcolm G. Anderson
Publisher: John Wiley & Sons
ISBN:
Category : Science
Languages : en
Pages : 522
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Book Description
Validation is a central issue to future model design in environmental science. This book is the first to provide a critical appraisal of today's validation needs, capabilities, and required changes in philosophy. It takes examples from four different scales: hillslope and river channel, catchment, regional, and global. This timely book offers unique, multifaceted coverage of model validation in hydrological science today. Topics covered include calibration procedures, data assimilation, scaling, critical future need in validation, and evidence of field data. * State-of-the-art research book on an important new topic * End-of-section discussion chapters written by leading international researchers
Author: Ewout W. Steyerberg
Publisher: Springer
ISBN: 3030163997
Category : Medical
Languages : en
Pages : 574
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Book Description
The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. Updates to this new and expanded edition include: • A discussion of Big Data and its implications for the design of prediction models • Machine learning issues • More simulations with missing ‘y’ values • Extended discussion on between-cohort heterogeneity • Description of ShinyApp • Updated LASSO illustration • New case studies
