الصفحة 4
الصفحة 4
Introduction to Bayesian Scientific Computing : Ten Lectures on Subjective Computing
Inverse problems are closely related to statistical inference problems, where the observations are used to infer on an underlying probability distribution. This connection between statistical inference and inverse problems is a central topic of the book. Inverse problems are typically ill-posed: small uncertainties in data may propagate in huge uncertainties in the estimates of the unknowns. To cope with such problems, efficient regularization techniques are developed in the framework of numerical analysis. The counterpart of regularization in the framework of statistical inference is the use prior information.
Interest Rate Models : an Infinite Dimensional Stochastic Analysis Perspective
Interest Rate Models: an Infinite Dimensional Stochastic Analysis Perspective studies the mathematical issues that arise in modeling the interest rate term structure. These issues are approached by casting the interest rate models as stochastic evolution equations in infinite dimensions. The book is comprised of three parts. Part I is a crash course on interest rates, including a statistical analysis of the data and an introduction to some popular interest rate models. Part II is a self-contained introduction to infinite dimensional stochastic analysis, including SDE in Hilbert spaces and Malliavin calculus. Part III presents some recent results in interest rate theory, including finite dimensional realizations of HJM models, generalized bond portfolios, and the ergodicity of HJM models.
Interacting Stochastic Systems
The Research Network on "Interacting stochastic systems of high complexity" set up by the German Research Foundation aimed at exploring and developing connections between research in infinite-dimensional stochastic analysis, statistical physics, spatial population models from mathematical biology, complex models of financial markets or of stochastic models interacting with other sciences. This book presents a structured collection of papers on the core topics, written at the close of the 6-year programme by the research groups who took part in it. The structure chosen highlights the interweaving of certain themes and certain interconnections discovered through the joint work.
Intelligent information processing and web mining ; Proceedings of the International IIS : IIPWM´06 Conference held in Ustron, Poland, June 19-22, 2006
This volume contains selected papers, presented at the international conference on Intelligent Information Processing and Web Mining Conference IIS:IIPWM'06, organized in Ustro« (Poland) on June 19-22nd, 2006. The submitted papers cover new computing paradigms, among others in biologically motivated methods, advanced data analysis, new machine learning paradigms, natural language processing, new optimization technologies, applied data mining using statistical and non-standard approaches.
Integrated circuit and system design : Power and timing modeling, optimization and simulation ; 17th International Workshop, PATMOS 2007, Gothenburg, Sweden, September 3-5, 2007, Proceedings
Papers cover high level design, low power design techniques, low power analog circuits, statistical static timing analysis, power modeling and optimization, low power routing optimization, security and asynchronous design, low power applications, modeling and optimization, and more.
Information theory and machine learning
The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges.
Information Processing with Evolutionary Algorithms : From Industrial Applications to Academic Speculations
The last decade of the 20th century has witnessed a surge of interest in num- ical, computation-intensive approaches to information processing. The lines that draw the boundaries among statistics, optimization, arti cial intelligence and information processing are disappearing, and it is not uncommon to nd well-founded and sophisticated mathematical approaches in application - mains traditionally associated with ad-hoc programming. Heuristics has - come a branch of optimization and statistics. Clustering is applied to analyze soft data and to provide fast indexing in the World Wide Web. Non-trivial matrix algebra is at the heart of the last advances in computer vision. The breakthrough impulse was, apparently, due to the rise of the interest in arti cial neural networks, after its rediscovery in the late 1980s. Disguised as ANN, numerical and statistical methods made an appearance in the - formation processing scene, and others followed. A key component in many intelligent computational processing is the search for an optimal value of some function. Sometimes, this function is not evident and it must be made explicit in order to formulate the problem as an optimization problem. The search - ten takes place in high-dimensional spaces that can be either discrete, or c- tinuous or mixed. The shape of the high-dimensional surface that corresponds to the optimized function is usually very complex. Evolutionary algorithms are increasingly being applied to information processing applications that require any kind of optimization.
Information Geometry : Near Randomness and Near Independence
This volume will be useful to practising scientists and students working in the application of statistical models to real materials or to processes with perturbations of a Poisson process, a uniform process, or a state of independence for a bivariate process. We use information geometry to provide a common differential geometric framework for a wide range of illustrative applications including amino acid sequence spacings in protein chains, cryptology studies, clustering of communications and galaxies, cosmological voids, coupled spatial statistics in stochastic fibre networks and stochastic porous media, quantum chaology. Introduction sections are provided to mathematical statistics, differential geometry and the information geometry of spaces of probability density functions.
Information extraction : Algorithms and prospects in a retrieval context
The book focuses on content recognition in text. It elaborates on the past and current most successful algorithms and their application in a variety of domains (e.g., news filtering, mining of biomedical text, intelligence gathering, competitive intelligence, legal information searching, and processing of informal text). An important part discusses current statistical and machine learning algorithms for information detection and classification and integrates their results in probabilistic retrieval models. The book also reveals a number of ideas towards an advanced understanding and synthesis of textual content.
Information criteria and statistical modeling
One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarz’s Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach.
Information and Complexity in Statistical Modeling
The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling.
Inference in Hidden Markov Models
This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states.In a unified way the book covers both models with finite state spaces, which allow for exact algorithms for filtering, estimation etc. and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Simulation in hidden Markov models is addressed in five different chapters that cover both Markov chain Monte Carlo and sequential Monte Carlo approaches. Many examples illustrate the algorithms and theory. The book also carefully treats Gaussian linear state-space models and their extensions and it contains a chapter on general Markov chain theory and probabilistic aspects of hidden Markov models.
Inference for change point and post change means after a CUSUM test
This monograph is the first to systematically study the bias of estimators and construction of corrected confidence intervals for change-point and post-change parameters after a change is detected by using a CUSUM procedure. Researchers in change-point problems and sequential analysis, time series and dynamic systems, and statistical quality control will find that the methods and techniques are mostly new and can be extended to more general dynamic models where the structural and distributional parameters are monitored. Practitioners, who are interested in applications to quality control, dynamic systems, financial markets, clinical trials and other areas, will benefit from case studies based on data sets from river flow, accident interval, stock prices, and global warming. Readers with an elementary probability and statistics background and some knowledge of CUSUM procedures will be able to understand most results as the material is relatively self-contained.The exponential family distribution is used as the basic model that includes changes in mean, variance, and hazard rate as special cases. There are fundamental differences between the sequential sampling plan and fixed sample size. Although the results are given under the CUSUM procedure, the methods and techniques discussed provide new approaches to deal with inference problems after sequential change-point detection, and they also contribute to the theoretical aspects of sequential analysis. Many results are of independent interests and can be used to study random walk related stochastic models.
Inference Control in Statistical Databases : From Theory to Practice
Inference control in statistical databases, also known as statistical disclosure limitation or statistical confidentiality, is about finding tradeoffs to the tension between the increasing societal need for accurate statistical data and the legal and ethical obligation to protect privacy of individuals and enterprises which are the source of data for producing statistics. Techniques used by intruders to make inferences compromising privacy increasingly draw on data mining, record linkage, knowledge discovery, and data analysis and thus statistical inference control becomes an integral part of computer science. This coherent state-of-the-art survey presents some of the most recent work in the field. The papers presented together with an introduction are organized in topical sections on tabular data protection, microdata protection, and software and user case studies.
Industrial competitiveness : Cost reduction
The objectives of industrial management are: - Implementation of the policy adopted by the owners or the board of directors - Optimum return on investment - Efficient utilization of Men, Machine and Money. In other words, industry must make profit. Manufacturing represents only one aspect of the activities of industrial management. Present-day manufacturing methodology does not consider making profit as their primary objective. The manufacturing process requires the knowledge of many disciplines, such as design, process planning, costing, marketing, sales, customer relations, costing, purchasing, bookkeeping, inventory control, material handling, shipping, and so on.
Inductive logic programming ; 18th International Conference, ILP 2008 Prague, Czech Republic, September 10-12, 2008 Proceedings
This book constitutes the refereed proceedings of the 18th International Conference on Inductive Logic Programming, ILP 2008, held in Prague, Czech Republic, in September 2008.The 20 revised full papers presented together with the abstracts of 5 invited lectures were carefully reviewed and selected during two rounds of reviewing and improvement from 46 initial submissions. All current topics in inductive logic programming are covered, ranging from theoretical and methodological issues to advanced applications. The papers present original results in the first-order logic representation framework, explore novel logic induction frameworks, and address also new areas such as statistical relational learning, graph mining, or the semantic Web.
Inductive logic programming ; 15th International Conference, ILP 2005, Bonn, Germany, August 10-13, 2005, Proceedings
“Change is inevitable.” Embracing this quote we have tried to carefully exp- iment with the format of this conference, the 15th International Conference on Inductive Logic Programming, hopefully making it even better than it already was. But it will be up to you, the inquisitive reader of this book, to judge our success. The major changes comprised broadening the scope of the conference to include more diverse forms of non-propositional learning, to once again have tutorials on exciting new areas, and, for the ?rst time, to also have a discovery challenge as a platform for collaborative work. This year the conference was co-located with ICML 2005, the 22nd Inter- tional Conference on Machine Learning, and also in close proximity to IJCAI 2005, the 19th International Joint Conference on Arti?cial Intelligence. - location can be tricky, but we greatly bene?ted from the local support provided by Codrina Lauth, Michael May, and others. We were also able to invite all ILP and ICML participants to shared events including a poster session, an invited talk, and a tutorial about the exciting new area of “statistical relational lea- ing”. Two more invited talks were exclusively given to ILP participants and were presented as a kind of stock-taking—?ttingly so for the 15th event in a series—but also tried to provide a recipe for future endeavours.
In and Out of Equilibrium 2
The intersection of probability and physics has been a rich and explosive area of growth in the past three decades, specifically covering such subjects as percolation theory, random walks in random environment, disordered systems, interacting particle systems and their many connections to statistical mechanics. The last decade was particularly fruitful for all these topics. This book reflects this development and marks also the first decade of the Brazilian School of Probability. This volume consists of a collection of invited articles, written by some of the most distinguished probabilists, most of whom have been personally responsible for advances in the various subfields of probability.
Implementing Models in Quantitative Finance : Methods and Cases
This book puts numerical methods into action for the purpose of solving concrete problems arising in quantitative finance. Part one develops a comprehensive toolkit including Monte Carlo simulation, numerical schemes for partial differential equations, stochastic optimization in discrete time, copula functions, transform-based methods and quadrature techniques. The content originates from class notes written for courses on numerical methods for finance and exotic derivative pricing held by the authors at Bocconi University since the year 2000. Part two proposes eighteen self-contained cases covering model simulation, derivative valuation, dynamic hedging, portfolio selection, risk management, statistical estimation and model calibration. It encompasses a wide variety of problems arising in markets for equity, interest rates, credit risk, energy and exotic derivatives.
Hypoelliptic estimates and spectral theory for Fokker-Planck operators and witten Laplacians
There has recently been a renewal of interest in Fokker-Planck operators, motivated by problems in statistical physics, in kinetic equations and differential geometry. Compared to more standard problems in the spectral theory of partial differential operators, those operators are not self-adjoint and only hypoelliptic. The aim of the analysis is to give, as generally as possible, an accurate qualitative and quantitative description of the exponential return to the thermodynamical equilibrium. While exploring and improving recent results in this direction this volume proposes a review of known techniques on: the hypoellipticity of polynomial of vector fields and its global counterpart; the global Weyl-Hörmander pseudo-differential calculus, the spectral theory of non-self-adjoint operators, the semi-classical analysis of Schrödinger-type operators.
