Results 211 to 220 of about 83,179 (266)
Some of the next articles are maybe not open access.
IEEE Transactions on Signal and Information Processing over Networks, 2022
Particle filtering provides an approximate representation of a tracked posterior density which converges asymptotically to the true posterior as the number of particles used increases. The greater the number of particles, the higher the computational complexity.
Praveen Babu Choppala +2 more
openaire +1 more source
Particle filtering provides an approximate representation of a tracked posterior density which converges asymptotically to the true posterior as the number of particles used increases. The greater the number of particles, the higher the computational complexity.
Praveen Babu Choppala +2 more
openaire +1 more source
2011
The search for similarities in large data sets has a very important role in many scientific fields. It permits to classify several types of data without an explicit information about it. In many cases researchers use analysis methodologies such as clustering to classify data with respect to the patterns and conditions together.
Ekaterina Nosova +3 more
openaire +4 more sources
The search for similarities in large data sets has a very important role in many scientific fields. It permits to classify several types of data without an explicit information about it. In many cases researchers use analysis methodologies such as clustering to classify data with respect to the patterns and conditions together.
Ekaterina Nosova +3 more
openaire +4 more sources
Resampled Regenerative Estimators
ACM Transactions on Modeling and Computer Simulation, 2015We discuss some estimators for simulations of processes having multiple regenerative sequences. The estimators are obtained by resampling trajectories without and with replacement, which correspond to a type of U -statistic and a type of V -statistic, respectively. The U
James M. Calvin, Marvin K. Nakayama
openaire +2 more sources
WIREs Data Mining and Knowledge Discovery, 2012
AbstractResampling methods are statistical procedures that reuse the sample data for the purpose of statistical inference. However, they do not require parametric assumptions that may be difficult to verify in practice. This focus article describes four resampling techniques, the bootstrap, the jackknife, crossâvalidation, and permutation tests ...
openaire +1 more source
AbstractResampling methods are statistical procedures that reuse the sample data for the purpose of statistical inference. However, they do not require parametric assumptions that may be difficult to verify in practice. This focus article describes four resampling techniques, the bootstrap, the jackknife, crossâvalidation, and permutation tests ...
openaire +1 more source
On resampling schemes for polytopes
Journal of Applied Probability, 2019AbstractThe convex hull of a sample is used to approximate the support of the underlying distribution. This approximation has many practical implications in real life. To approximate the distribution of the functionals of convex hulls, asymptotic theory plays a crucial role.
Weinan Qi, Mahmoud Zarepour
openaire +2 more sources
IEEE Transactions on Computers, 1982
Due to advances in VLSI technology, large scale arrays of microprocessors forming parallel processing systems have become feasible. The use of such a microprocessor array operating in the SIMD (single instruction stream-multiple data stream) mode to perform image resampling is explored.
Michael R. Warpenburg, Leah J. Siegel
openaire +1 more source
Due to advances in VLSI technology, large scale arrays of microprocessors forming parallel processing systems have become feasible. The use of such a microprocessor array operating in the SIMD (single instruction stream-multiple data stream) mode to perform image resampling is explored.
Michael R. Warpenburg, Leah J. Siegel
openaire +1 more source
Resampling for wireless access
Proceedings of PIMRC '96 - 7th International Symposium on Personal, Indoor, and Mobile Communications, 2002The well known problem among most random access protocols in wireless networks is that the throughput drops rapidly in heavy loads. To cope with this problem, one has to control to the load offered to a network. Unlike the traditional backoff policy in Ethernet where backoff occurs after collision, we propose various control schemes based on the new ...
Ming-Young You, Cheng-Shang Chang
openaire +1 more source
Resampling on a Pseudohexagonal Grid
CVGIP: Graphical Models and Image Processing, 1994Abstract This paper investigates resampling techniques on a pseudohexagonal grid. Hexagonal grids are known to be advantageous in many respects for sampling and representing digital images in various computer vision and graphics applications. Currently, a real hexagonal grid device is still difficult to find.
Innchyn Her, Chi-Tseng Yuan
openaire +1 more source
Resampling for Spatial Scalability
2006 International Conference on Image Processing, 2006Resampling is a fundamental issue in the design of a spatially scalable video codec. The resampling procedure is responsible for down-sampling the high-resolution video sequence to generate lower resolution data, as well as upsampling the transmitted lower resolution data to predict the original high-resolution frames.
C. Andrew Segall, Aggelos K. Katsaggelos
openaire +1 more source
On Importance Resampling for the Bootstrap
Biometrika, 1991SUMMARY We introduce an empirical method of importance resampling, which does not require analytical calculation of the resampling probabilities. Our method can easily be used as part of a general algorithm for Monte Carlo calculation of bootstrap confidence intervals and hypothesis tests.
KIM-ANH DO, PETER HALL
openaire +1 more source