Results 1 - 10 of 15,390

Approximating discrete probability distributions with dependence trees

by C. K. Chow, C. N. Liu - IEEE TRANSACTIONS ON INFORMATION THEORY , 1968
"... A method is presented to approximate optimally an n-dimensional discrete probability distribution by a product of second-order distributions, or the distribution of the first-order tree dependence. The problem is to find an optimum set of n-1 first order dependence relationship among the n variables ..."
Abstract - Cited by 881 (0 self) - Add to MetaCart
A method is presented to approximate optimally an n-dimensional discrete probability distribution by a product of second-order distributions, or the distribution of the first-order tree dependence. The problem is to find an optimum set of n-1 first order dependence relationship among the n

Parallel discrete event simulation

by Richard M. Fujimoto , 1990
"... Parallel discrete event simulation (PDES), sometimes I called distributed simulation, refers to the execution of a single discrete event simulation program on a parallel computer. PDES has attracted a considerable amount of interest in recent years. From a pragmatic standpoint, this interest arises ..."
Abstract - Cited by 818 (39 self) - Add to MetaCart
Parallel discrete event simulation (PDES), sometimes I called distributed simulation, refers to the execution of a single discrete event simulation program on a parallel computer. PDES has attracted a considerable amount of interest in recent years. From a pragmatic standpoint, this interest arises

Convolution Kernels on Discrete Structures

by David Haussler , 1999
"... We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs. The method can be applied iteratively to build a kernel on an infinite set from kernels involving generators of the set. The family of kernels generated generalizes the fa ..."
Abstract - Cited by 506 (0 self) - Add to MetaCart
We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs. The method can be applied iteratively to build a kernel on an infinite set from kernels involving generators of the set. The family of kernels generated generalizes

A public key cryptosystem and a signature scheme based on discrete logarithms

by Taher Elgamal - ADV. IN CRYPTOLOGY, SPRINGER-VERLAG , 1985
"... A new signature scheme is proposed, together with an implementation of the Diffie-Hellman key distribution scheme that achieves a public key cryptosystem. The security of both systems relies on the difficulty of computing discrete logarithms over finite fields. ..."
Abstract - Cited by 1551 (0 self) - Add to MetaCart
A new signature scheme is proposed, together with an implementation of the Diffie-Hellman key distribution scheme that achieves a public key cryptosystem. The security of both systems relies on the difficulty of computing discrete logarithms over finite fields.

Virtual time

by David R. Jefferson - ACM Transactions on Programming Languages and Systems , 1985
"... Virtual time is a new paradigm for organizing and synchronizing distributed systems which can be applied to such problems as distributed discrete event simulation and distributed database concur-rency control. Virtual time provides a flexible abstraction of real time in much the same way that virtua ..."
Abstract - Cited by 980 (7 self) - Add to MetaCart
Virtual time is a new paradigm for organizing and synchronizing distributed systems which can be applied to such problems as distributed discrete event simulation and distributed database concur-rency control. Virtual time provides a flexible abstraction of real time in much the same way

Reversible jump Markov chain Monte Carlo computation and Bayesian model determination

by Peter J. Green - Biometrika , 1995
"... Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some xed standard underlying measure. They have therefore not been available for application to Bayesian model determi ..."
Abstract - Cited by 1345 (23 self) - Add to MetaCart
Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some xed standard underlying measure. They have therefore not been available for application to Bayesian model

On Sequential Monte Carlo Sampling Methods for Bayesian Filtering

by Arnaud Doucet, Simon Godsill, Christophe Andrieu - STATISTICS AND COMPUTING , 2000
"... In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is develop ..."
Abstract - Cited by 1051 (76 self) - Add to MetaCart
In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework

Maximum Likelihood Phylogenetic Estimation from DNA Sequences with Variable Rates over Sites: Approximate Methods

by Ziheng Yang - J. Mol. Evol , 1994
"... Two approximate methods are proposed for maximum likelihood phylogenetic estimation, which allow variable rates of substitution across nucleotide sites. Three data sets with quite different characteristics were analyzed to examine empirically the performance of these methods. The first, called ..."
Abstract - Cited by 557 (29 self) - Add to MetaCart
the "discrete gamma model," uses several categories of rates to approximate the gamma distribution, with equal probability for each category. The mean of each category is used to represent all the rates falling in the category. The performance of this method is found to be quite good

Maximum entropy markov models for information extraction and segmentation

by Andrew McCallum, Dayne Freitag, Fernando Pereira , 2000
"... Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled as multinomial ..."
Abstract - Cited by 561 (18 self) - Add to MetaCart
as multinomial distributions over a discrete vocabulary, and the HMM parameters are set to maximize the likelihood of the observations. This paper presents a new Markovian sequence model, closely related to HMMs, that allows observations to be represented as arbitrary overlapping features (such as word

Solving multiclass learning problems via error-correcting output codes

by Thomas G. Dietterich, Ghulum Bakiri - JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH , 1995
"... Multiclass learning problems involve nding a de nition for an unknown function f(x) whose range is a discrete set containing k>2values (i.e., k \classes"). The de nition is acquired by studying collections of training examples of the form hx i;f(x i)i. Existing approaches to multiclass l ..."
Abstract - Cited by 726 (8 self) - Add to MetaCart
Multiclass learning problems involve nding a de nition for an unknown function f(x) whose range is a discrete set containing k>2values (i.e., k \classes"). The de nition is acquired by studying collections of training examples of the form hx i;f(x i)i. Existing approaches to multiclass
Results 1 - 10 of 15,390