Results 1 - 10 of 277,755

Maximum likelihood from incomplete data via the EM algorithm

by A. P. Dempster, N. M. Laird, D. B. Rubin - JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B , 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
Abstract - Cited by 11972 (17 self) - Add to MetaCart
A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value

A Singular Value Thresholding Algorithm for Matrix Completion

by Jian-Feng Cai, Emmanuel J. Candès, Zuowei Shen , 2008
"... This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task of reco ..."
Abstract - Cited by 555 (22 self) - Add to MetaCart
This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task

Efficient exact stochastic simulation of chemical systems with many species and many channels

by Michael A. Gibson, Jehoshua Bruck - J. Phys. Chem. A , 2000
"... There are two fundamental ways to view coupled systems of chemical equations: as continuous, represented by differential equations whose variables are concentrations, or as discrete, represented by stochastic processes whose variables are numbers of molecules. Although the former is by far more comm ..."
Abstract - Cited by 427 (5 self) - Add to MetaCart
common, systems with very small numbers of molecules are important in some applications (e.g., in small biological cells or in surface processes). In both views, most complicated systems with multiple reaction channels and multiple chemical species cannot be solved analytically. There are exact numerical

Tensor Decompositions and Applications

by Tamara G. Kolda, Brett W. Bader - SIAM REVIEW , 2009
"... This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or N -way array. Decompositions of higher-order tensors (i.e., N -way arrays with N ≥ 3) have applications in psychometrics, chemometrics, signal proce ..."
Abstract - Cited by 723 (18 self) - Add to MetaCart
This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or N -way array. Decompositions of higher-order tensors (i.e., N -way arrays with N ≥ 3) have applications in psychometrics, chemometrics, signal

Context-Aware Computing Applications

by Bill Schilit, Norman Adams, Roy Want , 1995
"... This paper describes systems that examine and react to an individual's changing context. Such systems can promote and mediate people's mleractlOns with devices, computers, and other people, and they can help navigate unfamiliar places. We bel1eve that a lunded amount of information coveTIn ..."
Abstract - Cited by 984 (6 self) - Add to MetaCart
TIng a per'son's proximale environment is most important for this form of com-puting since the interesting part of the world around us is what we can see, hear, and touch. In this paper we define context-aware computing, and describe four cal-egones of conteL·t-aware applications: proximate

Scalable Application Layer Multicast

by Suman Banerjee, Bobby Bhattacharjee, Christopher Kommareddy , 2002
"... We describe a new scalable application-layer multicast protocol, specifically designed for low-bandwidth, data streaming applications with large receiver sets. Our scheme is based upon a hierarchical clustering of the application-layer multicast peers and can support a number of different data deliv ..."
Abstract - Cited by 731 (21 self) - Add to MetaCart
We describe a new scalable application-layer multicast protocol, specifically designed for low-bandwidth, data streaming applications with large receiver sets. Our scheme is based upon a hierarchical clustering of the application-layer multicast peers and can support a number of different data

A closed-form solution for options with stochastic volatility with applications to bond and currency options

by Steven L. Heston - Review of Financial Studies , 1993
"... I use a new technique to derive a closed-form solution for the price of a European call option on an asset with stochastic volatility. The model allows arbitrary correlation between volatility and spotasset returns. I introduce stochastic interest rates and show how to apply the model to bond option ..."
Abstract - Cited by 1512 (6 self) - Add to MetaCart
to other problems. Many plaudits have been aptly used to describe Black and Scholes ’ (1973) contribution to option pricing theory. Despite subsequent development of option theory, the original Black-Scholes formula for a European call option remains the most successful and widely used application

Distance metric learning, with application to clustering with sideinformation,”

by Eric P Xing , Andrew Y Ng , Michael I Jordan , Stuart Russell - in Advances in Neural Information Processing Systems 15, , 2002
"... Abstract Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as K-means initially fails to find one that is meaningful to a user, the only recourse may be for ..."
Abstract - Cited by 818 (13 self) - Add to MetaCart
Abstract Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as K-means initially fails to find one that is meaningful to a user, the only recourse may

A tutorial on hidden Markov models and selected applications in speech recognition

by Lawrence R. Rabiner - PROCEEDINGS OF THE IEEE , 1989
"... Although initially introduced and studied in the late 1960s and early 1970s, statistical methods of Markov source or hidden Markov modeling have become increasingly popular in the last several years. There are two strong reasons why this has occurred. First the models are very rich in mathematical s ..."
Abstract - Cited by 5892 (1 self) - Add to MetaCart
structure and hence can form the theoretical basis for use in a wide range of applications. Sec-ond the models, when applied properly, work very well in practice for several important applications. In this paper we attempt to care-fully and methodically review the theoretical aspects of this type

Exact Sampling with Coupled Markov Chains and Applications to Statistical Mechanics

by James Gary Propp, David Bruce Wilson , 1996
"... For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain has ..."
Abstract - Cited by 543 (13 self) - Add to MetaCart
For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain
Results 1 - 10 of 277,755