Risk Model Validation
Author: Peter Quell
Publisher:
Published: 2016
Total Pages:
ISBN-13: 9781782722632
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The Analytics of Risk Model Validation
Author: George A. Christodoulakis
Publisher: Elsevier
Published: 2007-11-14
Total Pages: 217
ISBN-13: 0080553885
DOWNLOAD EBOOKRisk 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
Business Model Validation
Author: David Wanetick
Publisher:
Published: 2015-02-27
Total Pages: 376
ISBN-13: 9780692369562
DOWNLOAD EBOOKOver 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.
Verification and Validation in Scientific Computing
Author: William L. Oberkampf
Publisher: Cambridge University Press
Published: 2010-10-14
Total Pages: 782
ISBN-13: 1139491768
DOWNLOAD EBOOKAdvances 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.
The Validation of Risk Models
Author: S. Scandizzo
Publisher: Springer
Published: 2016-07-01
Total Pages: 242
ISBN-13: 1137436964
DOWNLOAD EBOOKThis 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.
Model Validation and Uncertainty Quantification, Volume 3
Author: Robert Barthorpe
Publisher: Springer
Published: 2018-07-30
Total Pages: 303
ISBN-13: 3319747932
DOWNLOAD EBOOKModel Validation and Uncertainty Quantification, Volume 3: Proceedings of the 36th IMAC, A Conference and Exposition on Structural Dynamics, 2018, 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
Building Machine Learning Pipelines
Author: Hannes Hapke
Publisher: "O'Reilly Media, Inc."
Published: 2020-07-13
Total Pages: 358
ISBN-13: 1492053147
DOWNLOAD EBOOKCompanies 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
Model Validation and Uncertainty Quantification, Volume 3
Author: Roland Platz
Publisher: Springer Nature
Published: 2023-10-06
Total Pages: 208
ISBN-13: 3031370031
DOWNLOAD EBOOKModel Validation and Uncertainty Quantification, Volume 3: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the third volume of ten 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: Introduction of Uncertainty Quantification Uncertainty Quantification in Dynamics Model Form Uncertainty and Selection incl. Round Robin Challenge Sensor and Information Fusion Virtual Sensing, Certification, and Real-Time Monitoring Surrogate Modeling
Model Validation and Uncertainty Quantification, Vol. 3
Author: Roland Platz
Publisher: Springer Nature
Published:
Total Pages: 147
ISBN-13: 3031688937
DOWNLOAD EBOOKModel Validation and Uncertainty Quantification, Volume 3
Author: Sez Atamturktur
Publisher: Springer
Published: 2016-06-27
Total Pages: 366
ISBN-13: 3319297546
DOWNLOAD EBOOKModel Validation and Uncertainty Quantifi cation, Volume 3. Proceedings of the 34th IMAC, A Conference and Exposition on Dynamics of Multiphysical Systems: From Active Materials to Vibroacoustics, 2016, the third volume of ten from the Conference brings together contributions to this important area of research and engineering. Th e collection presents early findings and case studies on fundamental and applied aspects of Structural Dynamics, including papers on: • Uncertainty Quantifi cation & Model Validation • Uncertainty Propagation in Structural Dynamics • Bayesian & Markov Chain Monte Carlo Methods • Practical Applications of MVUQ • Advances in MVUQ & Model Updating • Robustness in Design & Validation • Verifi cation & Validation Methods
