Results 1 - 10 of 24,081

FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem

by Michael Montemerlo, Sebastian Thrun, Daphne Koller, Ben Wegbreit - In Proceedings of the AAAI National Conference on Artificial Intelligence , 2002
"... The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter-base ..."
Abstract - Cited by 599 (10 self) - Add to MetaCart
-based algorithms, for example, require time quadratic in the number of landmarks to incorporate each sensor observation. This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number

The calculation of posterior distributions by data augmentation.

by Martin A Tanner , ; Wing , Hung Wong - Journal of the American Statistical Association , 1987
"... ..."
Abstract - Cited by 926 (10 self) - Add to MetaCart
Abstract not found

Markov chains for exploring posterior distributions

by Luke Tierney - Annals of Statistics , 1994
"... Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at ..."
Abstract - Cited by 1136 (6 self) - Add to MetaCart
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at

Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments

by John Geweke - IN BAYESIAN STATISTICS , 1992
"... Data augmentation and Gibbs sampling are two closely related, sampling-based approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither independent nor identically distributed complicates the assessment of convergence and numerical accurac ..."
Abstract - Cited by 604 (12 self) - Add to MetaCart
Data augmentation and Gibbs sampling are two closely related, sampling-based approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither independent nor identically distributed complicates the assessment of convergence and numerical

Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images.

by Stuart Geman , Donald Geman - IEEE Trans. Pattern Anal. Mach. Intell. , 1984
"... Abstract-We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice-like physical system. The assignment of an energy function in the physical system determines its Gibbs di ..."
Abstract - Cited by 5126 (1 self) - Add to MetaCart
mechanisms, including blurring, nonlinear deformations, and multiplicative or additive noise, the posterior distribution is an MRF with a structure akin to the image model. By the analogy, the posterior distribution defines another (imaginary) physical system. Gradual temperature reduction in the physical

Distributed space-time-coded protocols for exploiting cooperative diversity in wireless networks

by J. Nicholas Laneman, Gregory W. Wornell - IEEE TRANS. INF. THEORY , 2003
"... We develop and analyze space–time coded cooperative diversity protocols for combating multipath fading across multiple protocol layers in a wireless network. The protocols exploit spatial diversity available among a collection of distributed terminals that relay messages for one another in such a m ..."
Abstract - Cited by 622 (5 self) - Add to MetaCart
We develop and analyze space–time coded cooperative diversity protocols for combating multipath fading across multiple protocol layers in a wireless network. The protocols exploit spatial diversity available among a collection of distributed terminals that relay messages for one another in such a

Vogels, U-Net: a user-level network interface for parallel and distributed computing, in:

by Anindya Basu , Vineet Buch , Werner Vogels , Thorsten Von Eicken - Proceedings of the 15th ACM Symposium on Operating System Principles, ACM, , 1995
"... Abstract The U-Net communication architecture provides processes with a virtual view of a network device to enable user-level access to high-speed communication devices. The architecture, implemented on standard workstations using off-the-shelf ATM communication hardware, removes the kernel from th ..."
Abstract - Cited by 597 (17 self) - Add to MetaCart
the communication path, while still providing full protection. The model presented by U-Net allows for the construction of protocols at user level whose performance is only limited by the capabilities of network. The architecture is extremely flexible in the sense that traditional protocols like TCP and UDP

Learning Stochastic Logic Programs

by Stephen Muggleton , 2000
"... Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic context-free grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a first-order r ..."
Abstract - Cited by 1194 (81 self) - Add to MetaCart
-order range-restricted definite clause. This paper summarises the syntax, distributional semantics and proof techniques for SLPs and then discusses how a standard Inductive Logic Programming (ILP) system, Progol, has been modied to support learning of SLPs. The resulting system 1) nds an SLP with uniform

An analysis of transformations

by G. E. P. Box, D. R. Cox - Journal of the Royal Statistical Society. Series B (Methodological , 1964
"... In the analysis of data it is often assumed that observations y,, y,,...,y, are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal, homoscedasti ..."
Abstract - Cited by 1067 (3 self) - Add to MetaCart
, homoscedastic, linear model is appropriate after some suitable transformation has been applied to the y's. Inferences about the transformation and about the parameters of the linear model are made by computing the likelihood function and the relevant posterior distribution. The contributions of normality

Linear models and empirical bayes methods for assessing differential expression in microarray experiments.

by Gordon K Smyth , Gordon K Smyth - Stat. Appl. Genet. Mol. Biol. , 2004
"... Abstract The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. ..."
Abstract - Cited by 1321 (24 self) - Add to MetaCart
Abstract The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model
Results 1 - 10 of 24,081