Statistical multiple integration : proceedings of a Joint Summer Research Conference held at Humboldt University, June 17-23, 1989
Author(s)
Bibliographic Information
Statistical multiple integration : proceedings of a Joint Summer Research Conference held at Humboldt University, June 17-23, 1989
（Contemporary mathematics, 115）
American Mathematical Society, c1991
Available at / 60 libraries
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410.8-C86-115913017178//913017185,10092700490,10092700491,10092380049
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Hokkaido University, Library, Graduate School of Science, Faculty of Science and School of Science図書
DC19:519.5/F6682070193185
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Note
"The AMS-IMS-SIAM Joint Summer Research Conference on Statistical Multiple Integration was held at Humboldt University, Arcata, California, ... " -- T.p. verso
Includes bibliographical references
Description and Table of Contents
Description
High dimensional integration arises naturally in two major subfields of statistics: multivariate and Bayesian statistics. Indeed, the most common measures of central tendency, variation, and loss are defined by integrals over the sample space, the parameter space, or both. Recent advances in computational power have stimulated significant new advances in both Bayesian and classical multivariate statistics. In many statistical problems, however, multiple integration can be the major obstacle to solutions.This volume contains the proceedings of an AMS-IMS-SIAM Joint Summer Research Conference on Statistical Multiple Integration, held in June 1989 at Humboldt State University in Arcata, California. The conference represents an attempt to bring together mathematicians, statisticians, and computational scientists to focus on the many important problems in statistical multiple integration. The papers document the state of the art in this area with respect to problems in statistics, potential advances blocked by problems with multiple integration, and current work directed at expanding the capability to integrate over high dimensional surfaces.
Table of Contents
A survey of existing multidimensional quadrature routines by D. K. Kahaner Subregion adaptive algorithms for multiple integrals by A. Genz Parallel systems and adaptive integration by E. de Doncker and J. A. Kapenga High-dimensional numerical integration and massively parallel computing by M. Mascagni Multiple integration in Bayesian psychometrics by R. K. Tsutakawa Laplace's method in Bayesian analysis by R. E. Kass, L. Tierney, and J. B. Kadane Monte Carlo integration in Bayesian statistical analysis by R. L. Wolpert Generic, algorithmic approaches to Monte Carlo integration in Bayesian inference by J. Geweke Adaptive importance sampling and chaining by M. Evans Monte Carlo integration in general dynamic models by P. Muller Monte Carlo integration via importance sampling: Dimensionality effect and an adaptive algorithm by M.-S. Oh Comparison of simulation methods in the estimation of the ordered characteristic roots of a random covariance matrix by V. Luzar and I. Olkin A stationary stochastic approximation method by J. F. Monahan and R. F. Liddle Inequalities and bounds for a class of multiple probability integrals, with applications by Y. L. Tong A Gaussian cubature formula for the computation of generalized $B$-splines and its application to serial correlation by V. K. Kaishev Computational problems associated with minimizing the risk in a simple clinical trial by J. P. Hardwick Discussion on papers by Geweke, Wolpert, Evans, Oh, and Kass, Tierney, and Kadane by J. H. Albert Comments on computational conveniences discussed in articles by Evans, Geweke, Muller, and Kass-Tierney-Kadane by R. Shanmugam A discussion of papers by Genz, Tsutakawa, and Tong by I. Olkin A discussion of papers by Luzar and Olkin, Kaishev, and Monahan and Liddle by N. Flournoy.
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