Advanced Lectures in Mathematics, Vol. 16
Softcover. 299 pages.
ISBN: 978-1-57146-223-7
To be released: 15 July 2011
Transformation groups have played a fundamental role in many areas of
mathematics such as differential geometry, geometric topology, algebraic
topology, algebraic geometry, and number theory. One of the basic reasons
for their importance is that symmetries are described by groups (or rather,
by group actions). Quotients of smooth manifolds by group actions are
usually not smooth manifolds. On the other hand, if the actions of the
groups are proper, then the quotients are orbifolds. An important example is
given by the action of the mapping class groups on Teichmuller spaces: The
quotients give the moduli spaces of Riemann surfaces (or algebraic curves)
and are orbifolds.
This volume consists of expanded lecture notes from two summer schools given
in 2008: "Transformation Groups and Orbifolds" and "Geometry of Teichmuller
Spaces and Moduli Spaces." It should be a valuable source for study of
transformation groups, orbifolds, Teichmuller spaces, mapping class groups,
moduli spaces of curves, and related topics.
Advanced Lectures in Mathematics, Vol. 17
Softcover. 542 pages.
ISBN: 978-1-57146-224-4
To be released: 15 July 2011
Presented herein are parts 1 and 2 of a collection of substantial papers
presented by distinguished speakers at the conference "Geometric Analysis:
Present and Future," held at Harvard University in 2008. Among the speakers
were Edward Witten, Yum-Tong Siu, Richard Hamilton, Nigel Hitchin, Blaine
Lawson, Andrew Strominger, Cumrun Vafa, Wilfried Schmid, Victor Guillemin,
Ngaiming Mok, and Demetrios Christodoulou. Also included is an overview of
the works of Shing-Tung Yau.
This volume serves well as both a reference and up-to-date summary of
geometric analysis and its applications to many different areas of
mathematics.
Advanced Lectures in Mathematics, Vol. 18
Softcover. 563 pages.
ISBN: 978-1-57146-225-1
To be released: 15 July 2011
Publisher: International Press of Boston
2010 MSC: 58-06
List price: $85.00. Discounts may apply.
Presented herein are parts 3 and 4 of a collection of substantial papers
presented by distinguished speakers at the conference "Geometric Analysis:
Present and Future," held at Harvard University in 2008. Among the speakers
were Edward Witten, Yum-Tong Siu, Richard Hamilton, Nigel Hitchin, Blaine
Lawson, Andrew Strominger, Cumrun Vafa, Wilfried Schmid, Victor Guillemin,
Ngaiming Mok, and Demetrios Christodoulou. Also included is an overview of
the works of Shing-Tung Yau.
This volume serves well as both a reference and up-to-date summary of
geometric analysis and its applications to many different areas of
mathematics.
HARDBACK
9781421403526
312 pp., 24 line drawings
This valuable collection of essays by some of the world's leading scholars in mathematics presents innovative and field-defining work at the intersection of noncommutative geometry and number theory.
The interplay between these two fields centers on the study of the rich structure of the adele class space in noncommutative geometry, an important geometric space known to support and provide a geometric interpretation of the Riemann Weil explicit formulas in number theory. This space and the corresponding quantum statistical dynamical system are fundamental structures in the field of noncommutative geometry.
Several papers in this volume focus on the "field with one element" subject, a new topic in arithmetic geometry; others highlight recent developments in noncommutative geometry, illustrating unexpected connections with tropical geometry, idempotent analysis, and the theory of hyper-structures in algebra.
Originally presented at the Twenty-First Meeting of the Japan-U.S. Mathematics Institute, these essays collectively provide mathematicians and physicists with a comprehensive resource on the topic.
Caterina Consani is a professor in the Department of Mathematics at Johns Hopkins University. Alain Connes is a professor at the College de France, Institut des Hautes Etudes Scientifiques in Bures sur Yvette, and a distinguished professor in the Department of Mathematics at Vanderbilt University. He won the Fields Medal in 1982.
ISBN: 978-1-1180-2985-5
Hardcover
440 pages
September 2011
This book bridges the latest software applications with the benefits of modern resampling techniques
Resampling helps students understand the meaning of sampling distributions, sampling variability, P-vlaues, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques.
The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as:
*Exploratory data analysis
*Calculation of sampling distributions
*The Central Limit Theorem
*Monte Carlo sampling
*Maximum likelihood estimation and properties of estimators
*Confidence intervals and hypothesis
*Regression
*Bayesian methods
Throughout the book, case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the real-world applications of the discussed material. Key definitions and theorems of important probability distributions are collected at the end of the book, and a related website is also available, featuring additional material including data sets, R scripts, and helpful teaching hints.
Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. it also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work.
720 pages | 151 black and white line drawings | 234x156mm
978-0-19-969458-7 | Hardback | September 2011 (estimated)
State of the art Bayesian Statistics
New important applications
Authoritative reviews
Illuminating discussions
Broad international basis
The Valencia International Meetings on Bayesian Statistics - established in 1979 and held every four years - have been the forum for a definitive overview of current concerns and activities in Bayesian statistics. These are the edited Proceedings of the Ninth meeting, and contain the invited papers each followed by their discussion and a rejoinder by the authors(s). In the tradition of the earlier editions, this encompasses an enormous range of theoretical and applied research, high lighting the breadth, vitality and impact of Bayesian thinking in interdisciplinary research across many fields as well as the corresponding growth and vitality of core theory and methodology.
The Valencia 9 invited papers cover a broad range of topics, including foundational and core theoretical issues in statistics, the continued development of new and refined computational methods for complex Bayesian modelling, substantive applications of flexible Bayesian modelling, and new developments in the theory and methodology of graphical modelling. They also describe advances in methodology for specific applied fields, including financial econometrics and portfolio decision making, public policy applications for drug surveillance, studies in the physical and environmental sciences, astronomy and astrophysics, climate change studies, molecular biosciences, statistical genetics or stochastic dynamic networks in systems biology.
Readership: Suitable for statisticians and graduate students who want to keep up with new developments in the field and for scientists who want to learn about solutions to problems in their field not supplied by conventional statistics.
1: J. M. Bernardo: Integrated Objective Bayesian Estimation and Hypothesis Testing
2: C. M. Carvalho, H. F. Lopes, O. Aguilar: Dynamic Stock Selection Strategies: A Structured Factor Model Framework
3: Chopin, N. and Jacob, P.: Free Energy Sequential Monte Carlo, Application to Mixture Modelling
4: Consonni G. and La Rocca, L.: Moment Priors for Bayesian Model Choice with Applications to Directed Acyclic Graphs
5: Dunson, D. B. and Bhattacharya, A.: Nonparametric Bayes Regression and Classification Through Mixtures of Product Kernels
6: Fruhwirth-Schnatter, S. and Wagner, H.: Bayesian Variable Selection for Random Intercept Modeling of Gaussian and non-Gaussian Data.
7: Goldstein, M.: External Bayesian Analysis for Computer Simulators
8: Gramacy, R. B. and Lee, H. K. H.: Optimization Under Unknown Constraints
9: Huber, M. and Schott, S.: Using TPA for Bayesian Inference
10: Ickstadt, K., Bornkamp, B., Grzegorczyk, M., Wiecorek, J., Sherriff, M. R., Grecco, H. E. and Zamir, E.: Nonparametric Bayesian Networks
11: Lopes, H. F., Carvalho, C. M., Johannes, M. S. and Polson, N. G.: Particle Learning for Sequential Bayesian Computation
12: Loredo, T. J.: Rotating Stars and Revolving Planets: Bayesian Exploration of the Pulsating Sky
13: Louis, T. A., Carvalho, B. S., Fallin, M. D., Irizarryi, R. A., Li, Q. and Ruczinski, I.: Association Tests that Accommodate Genotyping Uncertainty
14: Madigan, D., Ryan, P., Simpson, S. and Zorych, I.: Bayesian Methods in Pharmacovigilance
15: Meek, C. and Wexler, Y.: Approximating Max-Sum-Product Problems using Multiplicative Error Bounds
16: Meng, X.-L.: What's the H in H-likelihood: A Holy Grail or an Achilles' Heel?
17: Polson, N. G. and Scott, J. G.: Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction
18: Richardson, S., Bottolo, L. and Rosenthal, J. S.: Bayesian Models for Sparse Regression Analysis of High Dimensional Data
19: Richardson, T. S., Evans, R. J. and Robins, J. M.: Transparent Parametrizations of Models for Potential Outcomes
20: Schmidt, A. M. and Rodriguez, M. A.: Modelling Multivariate Counts Varying Continuously in Space
21: Tebaldi, C., Sanso, B. and Smith, R. L.: Characterizing Uncertainty of Future Climate Change Projections using Hierarchical Bayesian Models
22: Vannucci, M. and Stingo, F. C.: Bayesian Models for Variable Selection that Incorporate Biological Information
23: Wilkinson, D. J.: Parameter Inference for Stochastic Kinetic Models
of Bacterial Gene Regulation: A Bayesian Approach to Systems Biolog