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Based upon ten case studies, Prediction explores how science-based predictions guide policy making and what this means in terms of global warming, biogenetically modifying organisms and polluting the environment with chemicals.
This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.
**Amazon Best Seller!** Reached #4 in Thrillers & Suspense ★★★★★ 'A captivating page-turner.' DOUGLAS WOLFE Nobody knows the day they’ll die… until now. Mathematical genius Daniel Geller has developed a formula to predict a person’s date of death, only to have it rejected by the faculty at Trinity College. Totally devastated he turns his back on the world he once loved. Twelve years on, Daniel’s old professor John Redmond and his wife are coming to terms with the death of their ten-year-old son. Could Daniel's formula have predicated his death? Revisiting the thesis, the professor makes an astonishing discovery: out of the five fellow students whom Daniel used the formula on, one of them died on the exact date he predicted. One more is due to die in six days: Daniel’s ex-lover Grace. The professor draws Daniel back into the world of mathematics where he is suddenly faced with the dilemma of allowing someone he once loved to die to be one step closer to proving his thesis and enjoying a prestige he once dreamed of… Set in the vibrant cities of Dublin and Amsterdam, The Prediction is a powerful story about coping with shattered dreams, the loss of a loved one, and an illustration of just how unpredictable the human heart can be. ____________________________________________ PRAISE FOR THE PREDICTION: 'Once you get hooked, you won't want to put the book down.' ALLISON JAMES 'There is something brilliant and enticing about a novel where one of the central conflicts is that you very much want for two mutually exclusive things to happen.' ANNE DOUCETTE 'I loved this book! It was emotionally intense, suspenseful, and so very touching and beautiful at the end. I cannot remember the last time a book brought me to tears...' JUDY SCHECHTER 'First Time Author Darren Sugrue hits the mark with a 5 star novel... This book is awesome.' L. FRIER 'The ending twist was just genius... I feel this is one of the few books anyone would enjoy no matter whether you are a romantic, thriller, horror or sci-fi reader.' GADGET GIRL REVIEWS 'Filled with suspense, peppered with a bit of romance and softened by tragedy, it is one of the best crime novels I have ever read... You will not hear this from me very often: this is a must-read! Readers of all genres, unite!' ANCA, REVIEWS WITH A TWIST BLOG 'Heart pounding suspense, lost love, regret, lost, murder, betrayal, it’s all there. Mind blowing plot twists that you have to pause to process... Drop everything, send the kids outside. This is an incredible read.' DOSEOFBELLA 'You really could not ask for more in a book. It is so well written it is hard to believe that this is Darren Sugrue's first book.' ANGIE, READAHOLIC ZONE 'The story is well written, moves at a good pace, with well-developed characters and a twist I really didn’t see coming.' JAMES WALSH
Explains the significance of each card in the tarot deck and tells how to use the cards to find advice or predict the future
In recent years, the banking industry has faced significant challenges due to deregulation, globalization, financial innovation, and intensified global competition. In response to these challenges, banks have adopted strategies to grow and expand their activities, with mergers and acquisitions (M & As) being one of the most popular over the last decade. This unique book thus discusses the use of quantitative classification methods for the prediction of bank acquisitions. With an overview of the M & A trends in the EU banking industry and a survey of the motives for M & As, the authors compare various statistical and computational methodologies used to analyze and predict bank acquisitions. The material constitutes a useful basis for researchers and practitioners in banking management to develop and analyze investment decisions related to M & As.
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. - Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning - Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering - Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection
This book presents a unified treatment of the prediction process approach to continuous time stochastic processes. The underling idea is that there are two kinds of time: stationary physical time and the moving observer's time. By developing this theme, the author develops a theory of stochastic processes whereby two processes are considered which coexist on the same probability space. In this way, the observer' process is strongly Markovian. Consequently, any measurable stochastic process of a real parameter may be regarded as a homogeneous strong Markov process in an appropriate setting. This leads to a unifying principle for the representation of general processes in terms of martingales which facilitates the prediction of their properties. While the ideas are advanced, the methods are reasonable elementary and should be accessible to readers with basic knowledge of measure theory, functional analysis, stochastic integration, and probability on the level of the convergence theorem for positive super-martingales.
Why seismologists still can't predict earthquakes An earthquake can strike without warning and wreak horrific destruction and death, whether it's the catastrophic 2010 quake that took a devastating toll on the island nation of Haiti or a future great earthquake on the San Andreas Fault in California, which scientists know is inevitable. Yet despite rapid advances in earthquake science, seismologists still can’t predict when the Big One will hit. Predicting the Unpredictable explains why, exploring the fact and fiction behind the science—and pseudoscience—of earthquake prediction. Susan Hough traces the continuing quest by seismologists to forecast the time, location, and magnitude of future quakes. She brings readers into the laboratory and out into the field—describing attempts that have raised hopes only to collapse under scrutiny, as well as approaches that seem to hold future promise. She also ventures to the fringes of pseudoscience to consider ideas outside the scientific mainstream. An entertaining and accessible foray into the world of earthquake prediction, Predicting the Unpredictable illuminates the unique challenges of predicting earthquakes.
Financial Statement Analysis and the Prediction of Financial Distress discusses the evolution of three main streams within the financial distress prediction literature: the set of dependent and explanatory variables used, the statistical methods of estimation, and the modeling of financial distress. Section 1 discusses concepts of financial distress. Section 2 discusses theories regarding the use of financial ratios as predictors of financial distress. Section 3 contains a brief review of the literature. Section 4 discusses the use of market price-based models of financial distress. Section 5 develops the statistical methods for empirical estimation of the probability of financial distress. Section 6 discusses the major empirical findings with respect to prediction of financial distress. Section 7 briefly summarizes some of the more relevant literature with respect to bond ratings. Section 8 presents some suggestions for future research and Section 9 presents concluding remarks.