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Extreme Value Modeling and Risk Analysis Methods and Applications

Extreme Value Modeling and Risk Analysis Methods and Applications

Extreme Value Modeling and Risk Analysis: Methods and Applications presents a broad overview of statistical modeling of extreme events along with the most recent methodologies and various applications. The book brings together background material and advanced topics eliminating the need to sort through the massive amount of literature on the subject. After reviewing univariate extreme value analysis and multivariate extremes the book explains univariate extreme value mixture modeling threshold selection in extreme value analysis and threshold modeling of non-stationary extremes. It presents new results for block-maxima of vine copulas develops time series of extremes with applications from climatology describes max-autoregressive and moving maxima models for extremes and discusses spatial extremes and max-stable processes. The book then covers simulation and conditional simulation of max-stable processes; inference methodologies such as composite likelihood Bayesian inference and approximate Bayesian computation; and inferences about extreme quantiles and extreme dependence. It also explores novel applications of extreme value modeling including financial investments insurance and financial risk management weather and climate disasters clinical trials and sports statistics. Risk analyses related to extreme events require the combined expertise of statisticians and domain experts in climatology hydrology finance insurance sports and other fields. This book connects statistical/mathematical research with critical decision and risk assessment/management applications to stimulate more collaboration between these statisticians and specialists. | Extreme Value Modeling and Risk Analysis Methods and Applications

GBP 44.99
1

Basic Matrix Algebra with Algorithms and Applications

Basic Matrix Algebra with Algorithms and Applications

Clear prose tight organization and a wealth of examples and computational techniques make Basic Matrix Algebra with Algorithms and Applications an outstanding introduction to linear algebra. The author designed this treatment specifically for freshman majors in mathematical subjects and upper-level students in natural resources the social sciences business or any discipline that eventually requires an understanding of linear models. With extreme pedagogical clarity that avoids abstraction wherever possible the author emphasizes minimal polynomials and their computation using a Krylov algorithm. The presentation is highly visual and relies heavily on work with a graphing calculator to allow readers to focus on concepts and techniques rather than on tedious arithmetic. Supporting materials including test preparation Maple worksheets are available for download from the Internet. This unassuming but insightful and remarkably original treatment is organized into bite-sized clearly stated objectives. It goes well beyond the LACSG recommendations for a first course while still implementing their philosophy and core material. Classroom tested with great success it prepares readers well for the more advanced studies their fields ultimately will require.

GBP 175.00
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Evaluating Climate Change Impacts

Evaluating Climate Change Impacts

Evaluating Climate Change Impacts discusses assessing and quantifying climate change and its impacts from a multi-faceted perspective of ecosystem social and infrastructure resilience given through a lens of statistics and data science. It provides a multi-disciplinary view on the implications of climate variability and shows how the new data science paradigm can help us to mitigate climate-induced risk and to enhance climate adaptation strategies. This book consists of chapters solicited from leading topical experts and presents their perspectives on climate change effects in two general areas: natural ecosystems and socio-economic impacts. The chapters unveil topics of atmospheric circulation climate modeling and long-term prediction; approach the problems of increasing frequency of extreme events sea level rise and forest fires as well as economic losses analysis of climate impacts for insurance agriculture fisheries and electric and transport infrastructures. The reader will be exposed to the current research using a variety of methods from physical modeling statistics and machine learning including the global circulation models (GCM) and ocean models statistical generalized additive models (GAM) and generalized linear models (GLM) state space and graphical models causality networks Bayesian ensembles a variety of index methods and statistical tests and machine learning methods. The reader will learn about data from various sources including GCM and ocean model outputs satellite observations and data collected by different agencies and research units. Many of the chapters provide references to open source software R and Python code that are available for implementing the methods.

GBP 54.99
1

Introduction to NFL Analytics with R

Introduction to NFL Analytics with R

It has become difficult to ignore the analytics movement within the NFL. An increasing number of coaches openly integrate advanced numbers into their game plans and commentators throughout broadcasts regularly use terms such as air yards CPOE and EPA on a casual basis. This rapid growth combined with an increasing accessibility to NFL data has helped create a burgeoning amateur analytics movement highlighted by the NFL’s annual Big Data Bowl. Because learning a coding language can be a difficult enough endeavor Introduction to NFL Analytics with R is purposefully written in a more informal format than readers of similar books may be accustomed to opting to provide step-by-step instructions in a structured jargon-free manner. Key Coverage: • Installing R RStudio and necessary packages • Working and becoming fluent in the tidyverse • Finding meaning in NFL data with examples from all the functions in the nflverse family of packages • Using NFL data to create eye-catching data visualizations • Building statistical models starting with simple regressions and progressing to advanced machine learning models using tidymodels and eXtreme Gradient Boosting The book is written for novices of R programming all the way to more experienced coders as well as audiences with differing expected outcomes. Professors can use Introduction to NFL Analytics with R to provide data science lessons through the lens of the NFL while students can use it as an educational tool to create robust visualizations and machine learning models for assignments. Journalists bloggers and arm-chair quarterbacks alike will find the book helpful to underpin their arguments by providing hard data and visualizations to back up their claims.

GBP 52.99
1

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques reinforced via realistic applications. The book is accessible and doesn’t prove theorems or dwell on mathematical theory. The goal is to present topics at an intuitive level with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth including Hidden Markov Models (HMM) Support Vector Machines (SVM) and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN) boosting Random Forests and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation Convolutional Neural Networks (CNN) Multilayer Perceptrons (MLP) and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented including Long Short-Term Memory (LSTM) Generative Adversarial Networks (GAN) Extreme Learning Machines (ELM) Residual Networks (ResNet) Deep Belief Networks (DBN) Bidirectional Encoder Representations from Transformers (BERT) and Word2Vec. Finally several cutting-edge deep learning topics are discussed including dropout regularization attention explainability and adversarial attacks. Most of the examples in the book are drawn from the field of information security with many of the machine learning and deep learning applications focused on malware. The applications presented serve to demystify the topics by illustrating the use of various learning techniques in straightforward scenarios. Some of the exercises in this book require programming and elementary computing concepts are assumed in a few of the application sections. However anyone with a modest amount of computing experience should have no trouble with this aspect of the book. Instructor resources including PowerPoint slides lecture videos and other relevant material are provided on an accompanying website: http://www. cs. sjsu. edu/~stamp/ML/.

GBP 62.99
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Large-Scale Machine Learning in the Earth Sciences

Large-Scale Machine Learning in the Earth Sciences

From the Foreword:While large-scale machine learning and data mining have greatly impacted a range of commercial applications their use in the field of Earth sciences is still in the early stages. This book edited by AshokSrivastava Ramakrishna Nemani and Karsten Steinhaeuser serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest…I hope that this book will inspire more computer scientists to focus on environmental applications and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences. Vipin Kumar University of MinnesotaLarge-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science computer science statistics and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources in the final chapter of the book.

GBP 44.99
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Artificial Intelligence and the Two Singularities

Artificial Intelligence and the Two Singularities

The science of AI was born a little over 60 years ago but for most of that time its achievements were modest. In 2012 it experienced a big bang when a branch of statistics called Machine Learning (and a sub-branch called Deep Learning) was applied to it. Now machines have surpassed humans in image recognition and they are catching up with us at speech recognition and natural language processing. Every day the media reports the launch of a new service a new product and a new demonstration powered by AI. When will it end? The surprising truth is the AI revolution has only just begun. Artificial Intelligence and the Two Singularities argues that in the course of this century the exponential growth in the capability of AI is likely to bring about two singularities - points at which conditions are so extreme that the normal rules break down. The first is the economic singularity when machine skill reaches a level that renders many of us unemployable and requires an overhaul of our current economic and social systems. The second is the technological singularity when machine intelligence reaches and then surpasses the cognitive abilities of an adult human relegating us to the second smartest species on the planet. These singularities will present huge challenges but this book argues that we can meet these challenges and overcome them. If we do the rewards could be almost unimaginable. This book covers: • Recent developments in AI and its future potential • The economic singularity and the technological singularity in depth • The risks and opportunities presented by AI • What actions we should take Artificial intelligence can turn out to be the best thing ever to happen to humanity making our future wonderful almost beyond imagination. But only if we address head-on the challenges that it will raise. Calum Chace is a best-selling author of fiction and non-fiction books and articles focusing on the subject of artificial intelligence. He is a regular speaker on artificial intelligence and related technologies and runs a blog on the subject at www. pandoras-brain. com. Prior to becoming a full-time writer and speaker he spent 30 years in business as a marketer a strategy consultant and a CEO. He studied philosophy at Oxford University where he discovered that the science fiction he had been reading since boyhood was simply philosophy in fancy dress.

GBP 46.99
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