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Hidden Markov Models for Bioinformatics
The purpose of this book is to give a thorough and systematic introduction to probabilistic modeling in bioinformatics. The book contains a mathematically strict and extensive presentation of the kind of probabilistic models that have turned out to be useful in genome analysis. Questions of parametric inference, selection between model families, and various architectures are treated. Several examples are given of known architectures (e.g., profile HMM) used in genome analysis. Audience: This book will be of interest to advanced undergraduate and graduate students with a fairly limited background in probability theory, but otherwise well trained in mathematics and already familiar with at least some of the techniques of algorithmic sequence analysis.
Bayesian Networks
Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout. Features include: An introduction to Dirichlet Distribution, Exponential Families and their applications. A detailed description of learning algorithms and Conditional Gaussian Distributions using Junction Tree methods. A discussion of Pearl's intervention calculus, with an introduction to the notion of see and do conditioning. All concepts are clearly defined and illustrated with examples and exercises. Solutions are provided online. This book will prove a valuable resource for postgraduate students of statistics, computer engineering, mathematics, data mining, artificial intelligence, and biology. Researchers and users of comparable modelling or statistical techniques such as neural networks will also find this book of interest.
Advances in Natural Computation
This is volume I of the proceedings of the Second International Conference on Natural Computation, ICNC 2006. After a demanding review process 168 carefully revised full papers and 86 revised short papers were selected from 1915 submissions for presentation in two volumes. This first volume includes 130 papers related to artificial neural networks, natural neural systems and cognitive science, neural network applications, as well as evolutionary computation: theory and algorithms.
Analysis
"Presenting the proceedings of the twenty-first Nordic Congress of Mathematicians at Lulearing; University of Technology, Sweden, this outstanding reference discusses recent advances in analysis, algebra, stochastic processes, and the use of computers in mathematical research."
Fisheries Review
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Point Processes and Jump Diffusions
Develop a deep understanding and working knowledge of point-process theory as well as its applications in finance.
Synergies of Soft Computing and Statistics for Intelligent Data Analysis
In recent years there has been a growing interest to extend classical methods for data analysis. The aim is to allow a more flexible modeling of phenomena such as uncertainty, imprecision or ignorance. Such extensions of classical probability theory and statistics are useful in many real-life situations, since uncertainties in data are not only present in the form of randomness --- various types of incomplete or subjective information have to be handled. About twelve years ago the idea of strengthening the dialogue between the various research communities in the field of data analysis was born and resulted in the International Conference Series on Soft Methods in Probability and Statistics (...
Wildlife Review
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Periodically Correlated Random Sequences
Uniquely combining theory, application, and computing, this bookexplores the spectral approach to time series analysis The use of periodically correlated (or cyclostationary)processes has become increasingly popular in a range of researchareas such as meteorology, climate, communications, economics, andmachine diagnostics. Periodically Correlated Random Sequencespresents the main ideas of these processes through the use of basicdefinitions along with motivating, insightful, and illustrativeexamples. Extensive coverage of key concepts is provided, includingsecond-order theory, Hilbert spaces, Fourier theory, and thespectral theory of harmonizable sequences. The authors also providea paradigm ...
