• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 428,352
Next 10 →

RINCÓN

by Juan A. Cardoso, Andy Jarvis, Michael Peters, John Miles, Miguel Ayarza, Henry Mateus, Jaime Quiceno
"... www.tropicalgrasslands.info Advances in improving tolerance to waterlogging in Brachiaria grasses ..."
Abstract - Add to MetaCart
www.tropicalgrasslands.info Advances in improving tolerance to waterlogging in Brachiaria grasses

Robust Monte Carlo Localization for Mobile Robots

by Sebastian Thrun, Dieter Fox, Wolfram Burgard, Frank Dellaert , 2001
"... Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), whi ..."
Abstract - Cited by 826 (88 self) - Add to MetaCart
Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples

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 1032 (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

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 1330 (24 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

Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics

by Geir Evensen - J. Geophys. Res , 1994
"... . A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The ..."
Abstract - Cited by 782 (22 self) - Add to MetaCart
. A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter

Bandit based Monte-Carlo Planning

by Levente Kocsis, Csaba Szepesvári - In: ECML-06. Number 4212 in LNCS , 2006
"... Abstract. For large state-space Markovian Decision Problems Monte-Carlo planning is one of the few viable approaches to find near-optimal solutions. In this paper we introduce a new algorithm, UCT, that applies bandit ideas to guide Monte-Carlo planning. In finite-horizon or discounted MDPs the algo ..."
Abstract - Cited by 433 (7 self) - Add to MetaCart
Abstract. For large state-space Markovian Decision Problems Monte-Carlo planning is one of the few viable approaches to find near-optimal solutions. In this paper we introduce a new algorithm, UCT, that applies bandit ideas to guide Monte-Carlo planning. In finite-horizon or discounted MDPs

A Long-Memory Property of Stock Market Returns and a New Model

by Zhuanxin Ding, Clive W. J. Granger, Robert F. Engle - Journal of Empirical Finance , 1993
"... A ‘long memory ’ property of stock market returns is investigated in this paper. It is found that not only there is substantially more correlation between absolute returns than returns them-selves, but the power transformation of the absolute return lrfl ” also has quite high autocorrel-ation for lo ..."
Abstract - Cited by 606 (21 self) - Add to MetaCart
A ‘long memory ’ property of stock market returns is investigated in this paper. It is found that not only there is substantially more correlation between absolute returns than returns them-selves, but the power transformation of the absolute return lrfl ” also has quite high autocorrel-ation

Estimation and Inference in Econometrics

by James G. Mackinnon , 1993
"... The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas o ..."
Abstract - Cited by 1151 (3 self) - Add to MetaCart
of bootstrap inference. The paper discusses Monte Carlo tests, several types of bootstrap test, and bootstrap confidence intervals. Although bootstrapping often works well, it does not do so in every case.

Lag length selection and the construction of unit root tests with good size and power

by Serena Ng, Pierre Perron - Econometrica , 2001
"... It is widely known that when there are errors with a moving-average root close to −1, a high order augmented autoregression is necessary for unit root tests to have good size, but that information criteria such as the AIC and the BIC tend to select a truncation lag (k) that is very small. We conside ..."
Abstract - Cited by 534 (14 self) - Add to MetaCart
It is widely known that when there are errors with a moving-average root close to −1, a high order augmented autoregression is necessary for unit root tests to have good size, but that information criteria such as the AIC and the BIC tend to select a truncation lag (k) that is very small. We

Incorporating non-local information into information extraction systems by gibbs sampling

by Jenny Rose Finkel, Trond Grenager, Christopher Manning - In ACL , 2005
"... Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, ..."
Abstract - Cited by 696 (25 self) - Add to MetaCart
, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference. We
Next 10 →
Results 1 - 10 of 428,352
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2018 The Pennsylvania State University