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Computational Pharmaceutical Solid State Chemistry
This book is the first to combine computational material science and modeling of molecular solid states for pharmaceutical industry applications. • Provides descriptive and applied state-of-the-art computational approaches and workflows to guide pharmaceutical solid state chemistry experiments and to support/troubleshoot API solid state selection • Includes real industrial case examples related to application of modeling methods in problem solving • Useful as a supplementary reference/text for undergraduate, graduate and postgraduate students in computational chemistry, pharmaceutical and biotech sciences, and materials science
Epidemic Modelling
This is a general introduction to the mathematical modelling of diseases.
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...
Ruin Probabilities
The book is a comprehensive treatment of classical and modern ruin probability theory. Some of the topics are Lundberg's inequality, the Cramér-Lundberg approximation, exact solutions, other approximations (eg. for heavy-tailed claim size distributions), finite horizon ruin probabilities, extensions of the classical compound Poisson model to allow for reserve-dependent premiums, Markov-modulation or periodicity. Special features of the book are the emphasis on change of measure techniques, phase-type distributions as a computational vehicle and the connection to other applied probability areas like queueing theory.
ARMA Model Identification
During the last two decades, considerable progress has been made in statistical time series analysis. The aim of this book is to present a survey of one of the most active areas in this field: the identification of autoregressive moving-average models, i.e., determining their orders. Readers are assumed to have already taken one course on time series analysis as might be offered in a graduate course, but otherwise this account is self-contained. The main topics covered include: Box-Jenkins' method, inverse autocorrelation functions, penalty function identification such as AIC, BIC techniques and Hannan and Quinn's method, instrumental regression, and a range of pattern identification methods. Rather than cover all the methods in detail, the emphasis is on exploring the fundamental ideas underlying them. Extensive references are given to the research literature and as a result, all those engaged in research in this subject will find this an invaluable aid to their work.
Mathematical Learning Models — Theory and Algorithms
This volume contains most of the contributions presented at the conference "Mathematical Learning Models - Theory and Algorithms". The conference was organized by the Institute of Applied Mathematics of the University of Bonn under the auspices of the Sonderforschungs bereich 72. It took place in the Physikzentrum in Bad Honnef near to Bonn from May 3 - May 7, 1982. The idea of the organizers was to bring together experts who work on very related problems, but partially by using different approaches. The main subjects of the program were: - mathematical learning models, - bandit problems, - stochastic approximation procedures, - sequential decision processes with unknown law of nature. We fe...
Recent Developments In Theoretical Studies Of Proteins
Experts provide a unique and broad perspective of the theoretical tools available today to analyze protein structure and function. Topics at the frontier of computational biophysics, such as dynamics and thermodynamics of proteins, reaction path studies, optimization techniques, analytical theories of protein folding, sequence alignment algorithms and electrostatics of proteins are discussed in a pedagogical and complete way. Those entering the field will find the book to be a useful introduction. It will also serve as a complementary text to existing ones that focus on just one of the above subjects.
Quasi-Likelihood And Its Application
The first account in book form of all the essential features of the quasi-likelihood methodology, stressing its value as a general purpose inferential tool. The treatment is rather informal, emphasizing essential principles rather than detailed proofs, and readers are assumed to have a firm grounding in probability and statistics at the graduate level. Many examples of the use of the methods in both classical statistical and stochastic process contexts are provided.
Selected Works of C.C. Heyde
In 1945, very early in the history of the development of a rigorous analytical theory of probability, Feller (1945) wrote a paper called “The fundamental limit theorems in probability” in which he set out what he considered to be “the two most important limit theorems in the modern theory of probability: the central limit theorem and the recently discovered ... ‘Kolmogoroff’s cel ebrated law of the iterated logarithm’ ”. A little later in the article he added to these, via a charming description, the “little brother (of the central limit theo rem), the weak law of large numbers”, and also the strong law of large num bers, which he considers as a close relative of the law of...
