Results 1  10
of
326,506
Analysing Probabilistically Constrained Optimism
"... In previous work we presented the DTRD algorithm, an optimistic synchronisation algorithm for parallel discrete event simulation of multiagent systems, and showed that it outperforms Time Warp and time windows on range of test cases. DTRD uses a decision theoretic model of rollback to derive an opt ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
an optimal time to delay read event so as to maximise the rate of LVT progression. The algorithm assumes that the interarrival times (both virtual and real) of events are normally distributed. In this paper we present a more detailed evaluation of the DTRD algorithm, and specifically how the performance
Mixtures of Probabilistic Principal Component Analysers
, 1998
"... Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a com ..."
Abstract

Cited by 537 (6 self)
 Add to MetaCart
maximumlikelihood framework, based on a specific form of Gaussian latent variable model. This leads to a welldefined mixture model for probabilistic principal component analysers, whose parameters can be determined using an EM algorithm. We discuss the advantages of this model in the context
Probabilistic Principal Component Analysis
 Journal of the Royal Statistical Society, Series B
, 1999
"... Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximumlikelihood estimation of paramet ..."
Abstract

Cited by 703 (5 self)
 Add to MetaCart
of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss, with illustrative examples, the advantages conveyed by this probabilistic approach
A Limited Memory Algorithm for Bound Constrained Optimization
 SIAM Journal on Scientific Computing
, 1994
"... An algorithm for solving large nonlinear optimization problems with simple bounds is described. ..."
Abstract

Cited by 557 (9 self)
 Add to MetaCart
An algorithm for solving large nonlinear optimization problems with simple bounds is described.
Constrained model predictive control: Stability and optimality
 AUTOMATICA
, 2000
"... Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and t ..."
Abstract

Cited by 696 (15 self)
 Add to MetaCart
Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence
Probabilistic Roadmaps for Path Planning in HighDimensional Configuration Spaces
 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
, 1996
"... A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collisionfree configurations and whose edg ..."
Abstract

Cited by 1276 (124 self)
 Add to MetaCart
A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collisionfree configurations and whose
2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late Nineteenth Century
 J. Geophysical Research
"... data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1 ° latitudelongitude grid ..."
Abstract

Cited by 517 (3 self)
 Add to MetaCart
from 1871. The companion HadMAT1 runs monthly from 1856 on a 5 ° latitudelongitude grid and incorporates new corrections for the effect on NMAT of increasing deck (and hence measurement) heights. HadISST1 and HadMAT1 temperatures are reconstructed using a twostage reducedspace optimal interpolation
The Optimal Degree of Commitment to an Intermediate Monetary Target
 QUARTERLY JOURNAL OF ECONOMICS
, 1985
"... ..."
Interior Point Methods in Semidefinite Programming with Applications to Combinatorial Optimization
 SIAM Journal on Optimization
, 1993
"... We study the semidefinite programming problem (SDP), i.e the problem of optimization of a linear function of a symmetric matrix subject to linear equality constraints and the additional condition that the matrix be positive semidefinite. First we review the classical cone duality as specialized to S ..."
Abstract

Cited by 557 (12 self)
 Add to MetaCart
We study the semidefinite programming problem (SDP), i.e the problem of optimization of a linear function of a symmetric matrix subject to linear equality constraints and the additional condition that the matrix be positive semidefinite. First we review the classical cone duality as specialized
Quantization Index Modulation: A Class of Provably Good Methods for Digital Watermarking and Information Embedding
 IEEE TRANS. ON INFORMATION THEORY
, 1999
"... We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing informationembedding rate, mini ..."
Abstract

Cited by 495 (15 self)
 Add to MetaCart
distortionrobustness tradeoffs than currently popular spreadspectrum and lowbit(s) modulation methods. Furthermore, we show that for some important classes of probabilistic models, DCQIM is optimal (capacityachieving) and regular QIM is nearoptimal. These include both additive white Gaussian noise
Results 1  10
of
326,506