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Modeling Uncertainty
Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications, is a volume undertaken by the friends and colleagues of Sid Yakowitz in his honor. Fifty internationally known scholars have collectively contributed 30 papers on modeling uncertainty to this volume. Each of these papers was carefully reviewed and in the majority of cases the original submission was revised before being accepted for publication in the book. The papers cover a great variety of topics in probability, statistics, economics, stochastic optimization, control theory, regression analysis, simulation, stochastic programming, Markov decision process, application in the HIV context, and others. There...
Recent Developments in Applied Probability and Statistics
This book is devoted to Professor Jürgen Lehn, who passed away on September 29, 2008, at the age of 67. It contains invited papers that were presented at the Wo- shop on Recent Developments in Applied Probability and Statistics Dedicated to the Memory of Professor Jürgen Lehn, Middle East Technical University (METU), Ankara, April 23–24, 2009, which was jointly organized by the Technische Univ- sität Darmstadt (TUD) and METU. The papers present surveys on recent devel- ments in the area of applied probability and statistics. In addition, papers from the Panel Discussion: Impact of Mathematics in Science, Technology and Economics are included. Jürgen Lehn was born on the 28th of April, ...
Handbook of Sequential Analysis
Sequential analysis refers to the body of statistical theory and methods where the sample size may depend in a random manner on the accumulating data. A formal theory in which optimal tests are derived for simple statistical hypotheses in such a framework was developed by Abraham Wald in the early 1
A Probabilistic Theory of Pattern Recognition
A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.
Lectures on Vanishing Theorems
Introduction M. Kodaira's vanishing theorem, saying that the inverse of an ample invert ible sheaf on a projective complex manifold X has no cohomology below the dimension of X and its generalization, due to Y. Akizuki and S. Nakano, have been proven originally by methods from differential geometry ([39J and [1]). Even if, due to J.P. Serre's GAGA-theorems [56J and base change for field extensions the algebraic analogue was obtained for projective manifolds over a field k of characteristic p = 0, for a long time no algebraic proof was known and no generalization to p > 0, except for certain lower dimensional manifolds. Worse, counterexamples due to M. Raynaud [52J showed that in characterist...
Measures of Complexity
This book brings together historical notes, reviews of research developments, fresh ideas on how to make VC (Vapnik–Chervonenkis) guarantees tighter, and new technical contributions in the areas of machine learning, statistical inference, classification, algorithmic statistics, and pattern recognition. The contributors are leading scientists in domains such as statistics, mathematics, and theoretical computer science, and the book will be of interest to researchers and graduate students in these domains.
Empirical Inference
This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the field blossom; and he is still as active as ever. He started analyzing learning algorithms in the 1960s and he invented the first version of the generalized portrait algorithm. He later developed one of the most successful methods in machine learning, the support vector machine (SVM) – more than just an algorithm, this was a new approach to learning problems, pioneering the use of functional ana...
The Saint and her Fool
On a magical Christmas Eve the impoverished Count Harro von Thorstein finds the young Princess Rosemarie wandering alone through the forest. She has come from Castle Brauneck fleeing from her golden cage in search of the love she desires and needs. Sensing a lost soul, much as himself, Harro gains the trust of the angelic child. A mystical bound of true love emerges, which holds them captive throughout their further lives. The young woman is granted with celestial strength, experiencing divine love and devotion to her belief. With sacred compassion she overcomes anguish and is lifted up to the hallowed purity of a saint.
Fairytales Slashed: Volume 2
In this second volume of fairy tales, see what happens when magic is put to the best and worst of uses. In The Beast (Megan Derr), a beautiful but spoiled young man is attacked by a dark faerie seeking vengeance... vengeance that destroys the young man's world, and leaves him hideously disfigured and afflicted by a terrible curse... The Wizard's Tower (Sasha L. Miller) is the story of a soldier who receives a letter informing him his brother has gone missing, one of many victims of an evil wizard. But when he arrives in the village where his brother lives, all that remains of the wizard is a lone tower and a strange young man... The Huntsman (Megan Derr) faithfully serves his King, guarding the forest that surrounds and protects the castle. But the peace his kingdom has long enjoyed is shattered when the King returns home early with a new Queen on his arm, a woman of great beauty and terrible magic... Sleeping Beauty(Sasha L. Miller) tells the story of a poor, young teacher desperate to save his sister, the only family he has left. But her disease is rare, and the cure beyond their means, and the only chance at saving her requires a terrible price...
Applications of Linear and Nonlinear Models
Here we present a nearly complete treatment of the Grand Universe of linear and weakly nonlinear regression models within the first 8 chapters. Our point of view is both an algebraic view as well as a stochastic one. For example, there is an equivalent lemma between a best, linear uniformly unbiased estimation (BLUUE) in a Gauss-Markov model and a least squares solution (LESS) in a system of linear equations. While BLUUE is a stochastic regression model, LESS is an algebraic solution. In the first six chapters we concentrate on underdetermined and overdeterimined linear systems as well as systems with a datum defect. We review estimators/algebraic solutions of type MINOLESS, BLIMBE, BLUMBE, ...
