Anton Bovier / Francois Dunlop / Aernout Van Enter /Frank Den Hollander
Jean Dalibard, Ph.D., Laboratoire Kastler Brossel, ENS, Paris, France.

MATHEMATICAL STATISTICAL PHYSICS, SESSION LXXXIII

Included in series
Les Houches Summer School Proceedings,

Description

The proceedings of the 2005 les Houches summer school on Mathematical Statistical Physics give and broad and clear overview on this fast developing area of interest to both physicists and mathematicians.

Audience

Libraries of mathematics and physics, Individual scientists

Contents
Chapter 1 - K. Johansson: Random Matrices and Determinantal Processes
Chapter 2 - W. Werner: Some Recent Aspects of Random Conformally Invariant Systems
Chapter 3 - B. Duplantier: Conformal Random Geometry
Chapter 4 - A.-S. Sznitman: Random Motions in Random Media
Chapter 5 - F. Guerra: An Introduction to Mean Field Spin Glas Theory: Methods and Results
Chapter 6 - C.M. Newman and D.L. Stein: Short-Range Spin Glasses: Selected Open Problems
Chapter 7 - G. Parisi: Computing the Number of Metastable States in Infinite-Range Models
Chapter 8 - Gerard Ben Arous and Jiri Cerny: Dynamics of Trap Models
Chapter 9 - N. Datta: Quantum Entropy and Quantum Information
Chapter 10 - A. Montanari: Two Lectures on Iterative Coding and Statistical Mechanics
Chapter 11 - A. Etheridge: Evolution in Fluctuating Populations
Chapter 12 - A. Greven: Multi-Scale Analysis of Population Models
Chapter 13 - Christian Maes: Elements of Nonequilibrium Statistical Mechanics
Chapter 14 - Frank Redig: Mathematical Aspects of the Abelian Sandpile Model
Chapter 15 - R. Fernandez: Gibbsianness and Non-Gibbsianness in Lattice Random Fields
Chapter 16 - A.D. Sokal: Simulation of Statistical Mechanics Models

Bibliographic & ordering Information

Hardbound, ISBN: 0-444-52813-X, 850 pages, publication date: 2006


B. Pachpatte, Marathwada University

INTEGRAL AND FINITE DIFFERENCE INEQUALITIES AND APPLICATIONS

Included in series
North-Holland Mathematics Studies, 205

Description

The monograph is written with a view to provide basic tools for researchers working in Mathematical Analysis and Applications, concentrating on differential, integral and finite difference equations. It contains many inequalities which have only recently appeared in the literature and which can be used as powerful tools and will be a valuable source for a long time to come. It is self-contained and thus should be useful for those who are interested in learning or applying the inequalities with explicit estimates in their studies.

- Contains a variety of inequalities discovered which find numerous applications in various branches of differential, integral and finite difference equations.
- Many inequalities which have only recently discovered in the literature and can not yet be found in bother book.
- A valuable reference for someone requiring results about inequalities for use in some applications in various other branches of mathematics.
- Will be of interest to researchers working both in pure and applied mathematics and other areas of science and technology, and it could also be used as a text for an advanced graduate course.

Contents

Preface
Introduction
Chapter 1. Integral inequalities in one variable
Chapter 2. Integral inequalities in two variables
Chapter 3. Retarded integral inequalities
Chapter 4. Finite difference inequalities in one variable
Chapter 5. Finite difference inequalities in two variables
References
Index

Bibliographic & ordering Information
Hardbound, ISBN: 0-444-52762-1, 320 pages, publication date: 2006


Edited By
Michel Chipot, University of Zurich, Zurich, Switserland.
Pavol Quittner, Comenius University, Bratislava, Slovakia.

HANDBOOK OF DIFFERENTIAL EQUATIONS:
STATIONARY PARTIAL DIFFERENTIAL EQUATIONS, 3

Description

This handbook is volume III in a series devoted to stationary partial differential quations. Similarly as volumes I and II, it is a collection of self contained state-of-the-art surveys written by well known experts in the field. The topics covered by this handbook include singular and higher order equations, problems near critically, problems with anisotropic nonlinearities, dam problem, T-convergence and Schauder-type estimates.
These surveys will be useful for both beginners and experts and speed up the progress of corresponding (rapidly developing and fascinating) areas of mathematics.

Key features:

- Written by well-known experts in the field
- Self-contained volume in series covering one of the most rapid developing topics in mathematics

Audience

Graduate students and academics

Contents

Preface
Contributors
1. S. Antontsev and S. Shmarev: Elliptic equations with anisotropic nonlinearity and nonstandard growth conditions.
2. A. Braides: A Handbook of T-convergence.
3. M. del Pino and M. Musso: Bubbling in nonlinear elliptic problems near criticality.
4. J. Hernandez and F.J. Mancebo: Singular elliptic and parabolic equations.
5. S. Kichenassamy: Schauder-type estimates and applications.
6. A. Lyaghfouri: The Dam problem.
7. L.A. Peletier: Nonlinear eigenvalue problems for higher order model equations.
Index

Bibliographic & ordering Information
Hardbound, ISBN: 0-444-52846-6, publication date: 2006


Edited By
A. Canada, University of Granada, Granada, Spain.
P. Drabek, University of West Bohemia, Pilsen, Czech Republic.
A. Fonda, University of Trieste, Trieste, Italy.

HANDBOOK OF DIFFERENTIAL EQUATIONS:
ORDINARY DIFFERENTIAL EQUATIONS, 3

Description

This handbook is the third volume in a series of volumes devoted to self contained and up-to-date surveys in the tehory of ordinary differential equations, written by leading researchers in the area. All contributors have made an additional effort to achieve readability for mathematicians and scientists from other related fields so that the chapters have been made accessible to a wide audience.

These ideas faithfully reflect the spirit of this multi-volume and hopefully it becomes a very useful tool for reseach, learing and teaching. This volumes consists of seven chapters covering a variety of problems in ordinary differential equations. Both pure mathematical research and real word applications are reflected by the contributions to this volume.

Key features

- Written by leading experts in the area
- Seven chapters covering a variety of problems in ordinary differential equations
- Pure mathematical and real word applications are well reflected

Audience

Mathematicians, Researchers, (post-)graduate students

Contents

Preface
1. Topological Principles for Ordinary Differential Equations (J. Andres).
2. Heteroclinic Orbtis for Some Classes of Second and Fourth Order Differential Equations (D. Bonheure and L. Sanchez).
3. A Qualitative Analysis, via Lower and Upper Solutions, of First Order Periodic Evolutionary Equations with Lack of Uniqueness (C. DeCoster, F. Obersne and P. Omari).
4. Bifurcation Theory of Limit Cycles of Planar Systems (M. Han).
5. Functional Differential Equations with State-Dependent Delays: Theory and Applications (F. Hartung, T. Krisztin, H.O. Walther and J. Wu).
6. Global Solutions Branches and Exact Multiplicity of Solutions for Two Point Boundary Value Problems (I. Rachunkova, S. Stanek and M. Tvrdy).
7. Singularities and Laplacians in Boundary Value Problems for Nonlinear Ordinary Differential Equations.
Index

Bibliographic & ordering Information

Hardbound, ISBN: 0-444-52849-0, 640 pages, publication date: 2006

George Roussas, University of California, Davis

AN INTRODUCTION TO PROBABILITY

Audience

This one-semester basic probability textbook is written for students in mathematics, physics, engineering, statistics, actuarial science, operations research, and computer science with a background in elementary calculus taking upper level or graduate level introduction to probability courses.

Contents

1. SOME MOTIVATING EXAMPLES 2. SOME FUNDAMENTAL CONCEPTS 3. THE CONCEPT OF PROBABILITY AND BASIC RESULTS 4. CONDITIONAL PROBABILITY AND INDEPENDENCE 5. NUMERICAL CHARACTERISTICS OF A RANDOM VARIABLE 6. SOME SPECIAL DISTRIBUTIONS 7. JOINT PROBABILITY DENSITY FUNCTION OF TWO RANDOM VARIABLES AND RELATED QUANTITIES 8. JOINT MOMENT GENERATING FUNCTION, COVARIANCE AND CORRELATION COEFFICIENT OF TWO RANDOM VARIABLES 9. SOME GENERALIZATIONS TO k RANDOM VARIABLES, AND THREE MULTIVARIATE DISTRIBUTIONS 10. INDEPENDENCE OF RANDOM VARIABLES AND SOME APPLICATIONS 11. TRANSFORMATION OF RANDOM VARIABLES 12. TWO MODES OF CONVERGENCE, THE WEAK LAW OF LARGE NUMBERS, THE CENTRAL LIMIT THEOREM, AND FURTHER RESULTS 13. AN OVERVIEW OF STATISTICAL INFERENCE APPENDIX TABLES 1. The Cumulative Binomial Distribution 2. The Cumulative Poisson Distribution 3. The Normal Distribution 4. Critical Values of the Chi-Square Distribution 5. Table of Selected Discrete and Continuous Distributions and Some of Their Characteristics 6. Handy Reference to Some Formulas Used in the Text SOME NOTATION AND ABBREVIATIONS ANSWERS TO THE EVEN-NUMBERED EXERCISES

Bibliographic & ordering Information

Hardbound, ISBN: 0-12-088595-6, 416 pages, publication date: 2007



Zhi Zong, Dalian University of Technology, Department of Naval Architecture, Dalian, China

INFORMATION-THEORETIC METHODS FOR ESTIMATING
OF COMPLICATED PROBABILITY DISTRIBUTIONS

Included in series
Mathematics in Science and Engineering, 207

Description

Mixing up various disciplines frequently produces something that are profound and far-reaching. Cybernetics is such an often-quoted example. Mix of information theory, statistics and computing technology proves to be very useful, which leads to the recent development of information-theory based methods for estimating complicated probability distributions.
Estimating probability distribution of a random variable is the fundamental task for quite some fields besides statistics, such as reliability, probabilistic risk analysis (PSA), machine learning, pattern recognization, image processing, neural networks and quality control. Simple distribution forms such as Gaussian, exponential or Weibull distributions are often employed to represent the distributions of the random variables under consideration, as we are taught in universities. In engineering, physical and social science applications, however, the distributions of many random variables or random vectors are so complicated that they do not fit the simple distribution forms at al.
Exact estimation of the probability distribution of a random variable is very important. Take stock market prediction for example. Gaussian distribution is often used to model the fluctuations of stock prices. If such fluctuations are not normally distributed, and we use the normal distribution to represent them, how could we expect our prediction of stock market is correct? Another case well exemplifying the necessity of exact estimation of probability distributions is reliability engineering. Failure of exact estimation of the probability distributions under consideration may lead to disastrous designs. There have been constant efforts to find appropriate methods to determine complicated distributions based on random samples, but this topic has never been systematically discussed in detail in a book or monograph. The present book is intended to fill the gap and documents the latest research in this subject.
Determining a complicated distribution is not simply a multiple of the workload we use to determine a simple distribution, but it turns out to be a much harder task. Two important mathematical tools, function approximation and information theory, that are beyond traditional mathematical statistics, are often used. Several methods constructed based on the two mathematical tools for distribution estimation are detailed in this book. These methods have been applied by the author for several years to many cases. They are superior in the following senses:
(1) No prior information of the distribution form to be determined is necessary. It can be determined automatically from the sample; (2) The sample size may be large or small; (3) They are particularly suitable for computers.
It is the rapid development of computing technology that makes it possible for fast estimation of complicated distributions.
The methods provided herein well demonstrate the significant cross influences between information theory and statistics, and showcase the fallacies of traditional statistics that, however, can be overcome by information theory.

Key Features:

- Density functions automatically determined from samples
- Free of assuming density forms
- Computation-effective methods suitable for PC

Audience
Statisticians and academic researchers.

Contents
Preface
Chapter 1. Randomness and probability
Chapter 2. Inference and statistics
Chapter 3. Random numbers and their applications
Chapter 4. Approximation and B-spline function
Chapter 5. Disorder, entropy and entropy estimation
Chapter 6. Estimation of 1-D complicated distributions based on large samples
Chapter 7. Estimation of 2-D complicated distributions based on large samples
Chapter 8. Estimation of 1-D complicated distribution based on small samples
Chapter 9. Estimation of 2-D complicated distribution based on small samples
Chapter 10. Estimation of the membership function
Chapter 11. Code specifications
Bibliography
Index

Bibliographic & ordering Information
Hardbound, ISBN: 0-444-52796-6, publication date: 2006