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This paper addresses the estimation of symmetric χ 2 -divergence between two point processes. ... We propose a novel approach by, first, mapping the space of spike trains in an appropriate functional space, and then, estimating the divergence in this functional space using a least square regression ... DISCUSSION In this paper, we have addressed the problem of computing the symmetric χ 2 -divergence between two point processes. ...doi:10.1109/icassp.2011.5946907 dblp:conf/icassp/ParkSRP11 fatcat:bgbl5arlxbb7jdzz7mdnnpzvoy
Previous work minimized the von Neuman divergence or its variant the total von Neumann divergence to estimate the parameters, ... Under a multivariate normality assumption on the distribution of the latent variables and of the errors, maximum likelihood estimators are asymptotically efficient. ... For a Chi-squared distribution centred about zero with six degrees of freedom and for a Chi-squared distribution centred about zero with ten degrees of freedom froblogt is best above 25% contamination. ...doi:10.21914/anziamj.v56i0.9359 fatcat:2uhkdzl4rrhwrp2e6ubhm3lk74
In this paper the authors apply the chi-square divergence measure STATISTICS 91k:62011 of information in a Bayesian set-up and use a Dirichlet process in a parametric way to define a nonparametric measure ... This reduction, analogous to the process of Rao-Blackwellization, may be used either to increase the power of a test, the efficiency of a point estimator, or alternatively, to render an inference function ...
We found that, for the conditions investigated, Least Square Estimation (LSE) is not an optimal statistical distance for the estimation of biophysical parameters. ... Our results indicate that other statistical distances, such as the two power measures, Hellinger, Pearson chi-squared measure, Arimoto and Koenker-Basset distances result in better estimates of biophysical ... Acknowledgements We thank Ana Prieto for advice and provision of LUT code. This work was funded by the NERC National Centre for Earth Observation (NCEO). ...doi:10.3390/rs5031355 fatcat:4phtxbsy4zahpcf4ezsa3oz3dm
The minimum chi-square estimators and their relationship to maximum quasi- likelihood estimators are also studied. James C. ... In this very detailed study, the authors consider the problem of curve estimation by means of kernel estimators for one-dimensional observations from the following point of view: since io and ho, the bandwidths ...
First, we introduce the proposed statistical hypothesis test based on the geodesic distance and on the fixed point estimation algorithm. ... Finally, the robust classification method is employed for the classification of simulated Polarimetric Synthetic Aperture Radar images of maritime pine forests. ... A well-known choice is the Kullback-Leibler (KL) divergence  , or its symmetric version: the Jeffreys divergence  . ...doi:10.1109/icip.2015.7350802 dblp:conf/icip/IleaBGTB15 fatcat:mz2anhdwkbgdxnbciiyjsx5tta
Lecture Notes in Computer Science
A statistical validation method based on analyzing residual error of the chi-square fitting is also proposed to complement this estimation framework. ... This system processes each 32-voxel volume-of-interest by an average of two seconds with a 2.4GHz Intel CPU. ... Acknowledgments The authors wish to thank Visvanathan Ramesh from Siemens Corporate Research for stimulating discussions, Alok Gupta from CAD group, Siemens Medical Solutions, for his support and encouragement ...doi:10.1007/978-3-540-24670-1_42 fatcat:e6v3agnikvdgxer25oqkxgi5pa
The proposed detection scheme is based on the application of Least Mean Square (LMS) filter and Pearson Chi-square divergence on randomly aggregated flows in Sketch data structure. ... Least mean square filter is used to predict the next value of the time series based on previous values, and Pearson Chisquare divergence is used to measure the deviations between the current and estimated ... As we want to compare the efficiency of Chi-square divergence with widely used divergence measures, we implement 3 distance measures over Sketch: Chi-square Divergence  , Hellinger Distance (HD [ ...doi:10.1109/lcn.2011.6115504 dblp:conf/lcn/SalemMTM11 fatcat:2ratigacgze67md2u3h7g5icci
Any symmetric function of canonical correlation coefficient can be used as a test statistic for testing independence. ... Most of the resulting test statistics lead to the chi-square test of Pearson. The third part deals with the problem of independence and correlation. ...
A new EI measure was developed for the sum of squared errors (SSE) which conforms to a generalised chi-square distribution and hence existing normal distribution-based EI measures are not applicable. ... However, computation has become a bottleneck when calibration of CFD models with experimental data (also known as model parameter estimation) is needed. ... must be estimated so the predictor becomes non-linear. 45 As such, the SSE conforms to a generalised chi-square distribution. 69 This can be seen as follows. ...doi:10.1002/aic.15352 fatcat:izqmkylzkze2xjluw5z7psk7uq
Vision Systems: Segmentation and Pattern Recognition
For each test object, we varied the visible object portion from 20% to 100% and recorded the recognition results using Chi-square divergence and a proposed method. ... The results show that a proposed method clearly obtains better results than Chi-square divergence. Using only 60% of the object area, almost 80% of the objects are still recognized. ... This chapter is distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike-3.0 License, which permits use, distribution and reproduction for non-commercial purposes, provided ...doi:10.5772/4973 fatcat:ea2zb6xd7vbrfahlavhjfamv3i
Specifically, we map the initial features (categorical attributes) to another space, using the Chi-square (C-S) as a measure of dissimilarity. ... Classification is an interesting approach for the recognition of this type of data. However, there are a few amount of methods for this purpose in the literature. ... Finally, we thank to the research group GAR of the Pontificia Universidad Javeriana Cali. Conflicts of Interest: The authors declare no conflict of interest. ...doi:10.3390/computation8040104 fatcat:m4u2q4koxraatfyt43xlqau6oe
ThêoH has narrower chi-square confidence than Avar; consequently, ThêoH provides significantly better determination of f noise types at long term. ... Furthermore, ThêoH has even narrower confidence than chi-square. ... points N x , and q p and q 1−p define the desired quantiles of the new distribution with p and 1 − p defined as described for chi-square in (5) . ...doi:10.1109/tuffc.2007.260 fatcat:hh6m4l7x65ekvhc7jrykn7ss7q
We introduce a new perspective on SVGD that instead views SVGD as the (kernelized) gradient flow of the chi-squared divergence which, we show, exhibits a strong form of uniform exponential ergodicity under ... Stein Variational Gradient Descent (SVGD), a popular sampling algorithm, is often described as the kernelized gradient flow for the Kullback-Leibler divergence in the geometry of optimal transport. ... We now prepare for the proof of exponentially fast convergence in chi-squared divergence for log-concave measures. ...arXiv:2006.02509v1 fatcat:rwnltchconc6dhcrejwte23pbe
We compare three divergence measures (Hellinger Distance, Chi-square and Power divergence) to analyze their detection accuracy. ... Our experimental results show that Power Divergence outperforms Chi-square divergence and Hellinger distance in network anomalies detection in terms of detection and false alarm. ... Power Divergence and Chi-square) The detection system records the number of monitored point (e.g. #packets, #SYN, #flows, etc.) in the Sketch for each discrete time interval T. ...doi:10.5121/ijwmn.2017.9301 fatcat:wfdzttfworcndhtix676xn2nqy
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