The Modern Law of Contract is a clear and logical textbook, written by an experienced author team with well over 50 years’ teaching and examining experience. Fully updated to address recent developments in Contract Law, it offers a carefully tailored overview of all key topics for LLB and GDL courses. The book also includes a number of learning features designed to enhance comprehension and aid exam preparation, allowing the reader to: ■■ understand and remember core topics: boxed chapter summaries offer a useful checklist for students, while illustrative diagrams help to clarify difficult concepts; ■■ identify important cases and assess their relevance: ‘Key case’ features highlight and contextualise the most significant cases; ■■ reflect on how contract law operates in context: highlighted ‘For thought’ features ask students to consider ‘what if’ scenarios, while ‘In focus’ features offer critical commentary on the law; ■■ consolidate learning and prepare for assessment: further reading lists and companion website directions at the end of each chapter direct you to additional interactive resources to test and reinforce your knowledge. Clearly written and easy to use, The Modern Law of Contract enables undergraduate students of contract law to fully engage with the topic and gain a profound understanding of this fundamental area.
This thoroughly revised second edition provides a clear overview of the functions and liabilities of insolvency practitioners (IPs). It considers the circumstances in which IPs are appointed, their duties and their powers, before offering a detailed investigation into their potential professional liabilities, as well as in-depth guidance to practitioners and advisers as to how claims might be framed and defended.
Presents the Bayesian approach to statistical signal processing for a variety of useful model sets This book aims to give readers a unified Bayesian treatment starting from the basics (Baye’s rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on “Sequential Bayesian Detection,” a new section on “Ensemble Kalman Filters” as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to “fill-in-the gaps” of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical “sanity testing” lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed and applied. These expansions of the book have been updated to provide a more cohesive discussion of Bayesian processing with examples and applications enabling the comprehension of alternative approaches to solving estimation/detection problems. The second edition of Bayesian Signal Processing features: “Classical” Kalman filtering for linear, linearized, and nonlinear systems; “modern” unscented and ensemble Kalman filters: and the “next-generation” Bayesian particle filters Sequential Bayesian detection techniques incorporating model-based schemes for a variety of real-world problems Practical Bayesian processor designs including comprehensive methods of performance analysis ranging from simple sanity testing and ensemble techniques to sophisticated information metrics New case studies on adaptive particle filtering and sequential Bayesian detection are covered detailing more Bayesian approaches to applied problem solving MATLAB® notes at the end of each chapter help readers solve complex problems using readily available software commands and point out other software packages available Problem sets included to test readers’ knowledge and help them put their new skills into practice Bayesian Signal Processing, Second Edition is written for all students, scientists, and engineers who investigate and apply signal processing to their everyday problems.
A unique treatment of signal processing using a model-based perspective Signal processing is primarily aimed at extracting useful information, while rejecting the extraneous from noisy data. If signal levels are high, then basic techniques can be applied. However, low signal levels require using the underlying physics to correct the problem causing these low levels and extracting the desired information. Model-based signal processing incorporates the physical phenomena, measurements, and noise in the form of mathematical models to solve this problem. Not only does the approach enable signal processors to work directly in terms of the problem's physics, instrumentation, and uncertainties, but it provides far superior performance over the standard techniques. Model-based signal processing is both a modeler's as well as a signal processor's tool. Model-Based Signal Processing develops the model-based approach in a unified manner and follows it through the text in the algorithms, examples, applications, and case studies. The approach, coupled with the hierarchy of physics-based models that the author develops, including linear as well as nonlinear representations, makes it a unique contribution to the field of signal processing. The text includes parametric (e.g., autoregressive or all-pole), sinusoidal, wave-based, and state-space models as some of the model sets with its focus on how they may be used to solve signal processing problems. Special features are provided that assist readers in understanding the material and learning how to apply their new knowledge to solving real-life problems. * Unified treatment of well-known signal processing models including physics-based model sets * Simple applications demonstrate how the model-based approach works, while detailed case studies demonstrate problem solutions in their entirety from concept to model development, through simulation, application to real data, and detailed performance analysis * Summaries provided with each chapter ensure that readers understand the key points needed to move forward in the text as well as MATLAB(r) Notes that describe the key commands and toolboxes readily available to perform the algorithms discussed * References lead to more in-depth coverage of specialized topics * Problem sets test readers' knowledge and help them put their new skills into practice The author demonstrates how the basic idea of model-based signal processing is a highly effective and natural way to solve both basic as well as complex processing problems. Designed as a graduate-level text, this book is also essential reading for practicing signal-processing professionals and scientists, who will find the variety of case studies to be invaluable. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department
A bridge between the application of subspace-based methods for parameter estimation in signal processing and subspace-based system identification in control systems Model-Based Processing: An Applied Subspace Identification Approach provides expert insight on developing models for designing model-based signal processors (MBSP) employing subspace identification techniques to achieve model-based identification (MBID) and enables readers to evaluate overall performance using validation and statistical analysis methods. Focusing on subspace approaches to system identification problems, this book teaches readers to identify models quickly and incorporate them into various processing problems including state estimation, tracking, detection, classification, controls, communications, and other applications that require reliable models that can be adapted to dynamic environments. The extraction of a model from data is vital to numerous applications, from the detection of submarines to determining the epicenter of an earthquake to controlling an autonomous vehicles—all requiring a fundamental understanding of their underlying processes and measurement instrumentation. Emphasizing real-world solutions to a variety of model development problems, this text demonstrates how model-based subspace identification system identification enables the extraction of a model from measured data sequences from simple time series polynomials to complex constructs of parametrically adaptive, nonlinear distributed systems. In addition, this resource features: Kalman filtering for linear, linearized, and nonlinear systems; modern unscented Kalman filters; as well as Bayesian particle filters Practical processor designs including comprehensive methods of performance analysis Provides a link between model development and practical applications in model-based signal processing Offers in-depth examination of the subspace approach that applies subspace algorithms to synthesized examples and actual applications Enables readers to bridge the gap from statistical signal processing to subspace identification Includes appendices, problem sets, case studies, examples, and notes for MATLAB Model-Based Processing: An Applied Subspace Identification Approach is essential reading for advanced undergraduate and graduate students of engineering and science as well as engineers working in industry and academia.
Bankruptcy attorneys will be intrigued by this early comprehensive treatment of the law governing voluntary assignments for the benefit of creditors. i.e., transfers not under the compulsion of law by debtors of their property in trust for the payment of their debts. Presentation of the subject is made in the time order in which its various aspects occur. Thousands of cases are cited along the way.
Presents the Bayesian approach to statistical signal processing for a variety of useful model sets This book aims to give readers a unified Bayesian treatment starting from the basics (Baye’s rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on “Sequential Bayesian Detection,” a new section on “Ensemble Kalman Filters” as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to “fill-in-the gaps” of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical “sanity testing” lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed and applied. These expansions of the book have been updated to provide a more cohesive discussion of Bayesian processing with examples and applications enabling the comprehension of alternative approaches to solving estimation/detection problems. The second edition of Bayesian Signal Processing features: “Classical” Kalman filtering for linear, linearized, and nonlinear systems; “modern” unscented and ensemble Kalman filters: and the “next-generation” Bayesian particle filters Sequential Bayesian detection techniques incorporating model-based schemes for a variety of real-world problems Practical Bayesian processor designs including comprehensive methods of performance analysis ranging from simple sanity testing and ensemble techniques to sophisticated information metrics New case studies on adaptive particle filtering and sequential Bayesian detection are covered detailing more Bayesian approaches to applied problem solving MATLAB® notes at the end of each chapter help readers solve complex problems using readily available software commands and point out other software packages available Problem sets included to test readers’ knowledge and help them put their new skills into practice Bayesian Signal Processing, Second Edition is written for all students, scientists, and engineers who investigate and apply signal processing to their everyday problems.
Employment Law has been developed primarily for students taking an elective module in employment law on the LPC and is suitable for courses with either a corporate or private client focus. The 2014 edition continues to provide a practical and comprehensive guide to the subject and has been fully updated to include recent UK and European case law and developments in employment law practice. Examples and sample documents are included throughout the book to help students understand the practical application of the law, preparing them for the situations they may encounter once qualified. Detailed information is presented clearly and concisely, with the use of flowcharts and diagrams to provide a visual overview of complex processes and areas of common difficulty. End of chapter summaries and self-test questions are also used throughout the book, to help students consolidate their learning and identify areas for further study. This book is also accompanied by a free Online Resource Centre (www.oxfordtextbooks.co.uk/orc/employment2014/) which includes updates to the law post-publication, self-test questions with instant feedback, outline answers to the questions in the book, and electronic versions of flowcharts and diagrams to assist with notes and revision.
Featuring case study questions and exercises, this practical and accessible guide is particularly suitable for students taking employment law as part of their legal practice course.
Employment Law has been developed primarily for students taking an elective module in employment law on the LPC and is suitable for courses with either a corporate or private client focus. The 2015 edition continues to provide a practical and comprehensive guide to the subject and has been fully updated to include recent UK and European case law and developments in employment law practice. Examples and sample documents are included throughout the book to help students understand the practical application of the law, preparing them for the situations they may encounter once qualified. Detailed information is presented clearly and concisely, with the use of flowcharts and diagrams to provide a visual overview of complex processes and areas of common difficulty. End of chapter summaries and self-test questions are also used throughout the book, to help students consolidate their learning and identify areas for further study. This book is also accompanied by a free Online Resource Centre (www.oxfordtextbooks.co.uk/orc/employment2015/) which includes updates to the law post-publication, self-test questions with instant feedback, outline answers to the questions in the book, and electronic versions of flowcharts and diagrams to assist with notes and revision.
Employment Law has been developed primarily for students taking an elective module in employment law on the LPC and is suitable for courses with either a corporate or private client focus. The 2016 edition continues to provide a practical and comprehensive guide to the subject and has been fully updated to include recent UK and European case law and developments in employment law practice. Examples and sample documents are included throughout the book to help students understand the practical application of the law, preparing them for the situations they may encounter once qualified. Detailed information is presented clearly and concisely, with the use of flowcharts and diagrams to provide a visual overview of complex processes and areas of common difficulty. End of chapter summaries and self-test questions are also used throughout the book, to help students consolidate their learning and identify areas for further study. This book is also accompanied by a free Online Resource Centre (www.oxfordtextbooks.co.uk/orc/employment2016/) which includes updates to the law post-publication, self-test questions with instant feedback, outline answers to the questions in the book, and electronic versions of flowcharts and diagrams to assist with notes and revision.
Featuring case study questions and exercises, this practical and accessible guide is particularly suitable for students taking employment law as part of their legal practice course.
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