Results 151 to 160 of about 1,207 (171)

An Optimal Transport Perspective on Gamma Gaussian Inverse-Wishart Mixture Reduction

2022 25th International Conference on Information Fusion (FUSION), 2022
Recent advances in the Optimal Transport theory allow to rewrite several known problems in a neat way, while providing a more general perspective. When dealing with mixture densities, or in general with intensities, such a framework naturally induces composite dissimilarities, together with corresponding Greedy Reduction and Refinement algorithms.
D'Ortenzio A., Manes C., Orguner U.
exaly   +6 more sources

Hyper Inverse Wishart Distribution for Non‐decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models

Scandinavian Journal of Statistics, 2002
While conjugate Bayesian inference in decomposable Gaussian graphical models is largely solved, the non‐decomposable case still poses difficulties concerned with the specification of suitable priors and the evaluation of normalizing constants. In this paper we derive the DY‐conjugate prior (Diaconis & Ylvisaker, 1979) for non‐decomposable models ...
Alberto Roverato
exaly   +5 more sources

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Variational Adaptive Kalman Filter With Gaussian-Inverse-Wishart Mixture Distribution

IEEE Transactions on Automatic Control, 2021
In this article, a new variational adaptive Kalman filter with Gaussian-inverse-Wishart mixture distribution is proposed for a class of linear systems with both partially unknown state and measurement noise covariance matrices. The state transition and measurement likelihood probability density functions are described by a Gaussian-inverse-Wishart ...
Yulong Huang 0003, Yonggang Zhang 0001, Peng Shi 0001, Jonathon A. Chambers   +3 more
openaire   +4 more sources

Generalized Inverse Gaussian Distributions and their Wishart Connections

Scandinavian Journal of Statistics, 1998
The matrix generalized inverse Gaussian distribution (MGIG) is shown to arise as a conditional distribution of components of a Wishart distributio n. In the special scalar case, the characterization refers to members of the class of generalized inverse Gaussian distributions (GIGs) and includes the inverse Gaussian distribution among ...
Ronald W Butler
openaire   +4 more sources

A global difference measure for the reduction of Gaussian inverse Wishart mixtures

Signal Processing, 2013
This paper presents an evaluation criterion, called a global difference measure, for the reduction of Gaussian inverse Wishart (GIW) mixtures. It is a deviation between the original and reduced GIW mixture, in other words, a numerical way describing the performance of the reduction algorithm instead of just a previous curve analysis (i.e., visual ...
Yongquan Zhang, Hongbing Ji
openaire   +3 more sources

A robust and fast partitioning algorithm for extended target tracking using a Gaussian inverse Wishart PHD filter

Knowledge-Based Systems, 2016
Extended target Gaussian inverse Wishart probability hypothesis density (ET-GIW-PHD) filter is a promising filter. However, the exact filter requires all possible partitions of the current measurement set for updating, which is computationally intractable.
Yongquan Zhang, Hongbing Ji
openaire   +3 more sources

Modified Gaussian inverse Wishart PHD filter for tracking multiple non-ellipsoidal extended targets

Signal Processing, 2018
Abstract Use of the Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter has demonstrated promise as an approach used to track an unknown number of ellipsoidal extended targets. However, based on the assumption that the shape of a target is not ellipsoidal, the performance of the GIW-PHD filter will degrade. This study presents an
Peng Li 0076, Hong-Wei Ge, Jinlong Yang 0002, Wenhui Wang   +3 more
openaire   +3 more sources

Gamma Gaussian Inverse Wishart Probability Hypothesis Density for Extended Target Tracking Using X-Band Marine Radar Data

IEEE Transactions on Geoscience and Remote Sensing, 2015
X-band marine radar systems represent a flexible and low-cost tool for the tracking of multiple targets in a given region of interest. Although suffering several sources of interference, e.g., the sea clutter, these systems can provide high-resolution measurements, both in space and time.
Granstrom K, Natale A, Braca P, Ludeno G, Serafino F   +4 more
openaire   +3 more sources

Permutation-Free Cgmm: Complex Gaussian Mixture Model with Inverse Wishart Mixture Model Based Spatial Prior for Permutation-Free Source Separation and Source Counting

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Here we propose a permutation-free cGMM (PF-cGMM), a new probabilistic model of observed mixtures, which can resolve permutation ambiguity between frequency bins, and is applicable even when the number of sources is unknown. A recently proposed complex Gaussian mixture model (cGMM) is highly effective for frequency bin-wise clustering when the number ...
Juan Azcarreta, Nobutaka Ito, Shoko Araki, Tomohiro Nakatani   +3 more
openaire   +3 more sources

A Partitioning Method for Gamma Gaussian inverse Wishart Probability Hypothesis Density Filter using Kolmogorov-Smirnov Test

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE), 2021
Cheng Chen, Peng Li, Junnan Zhang, Haoyue Chen, Yueyu Ma, Qi Zhao   +5 more
openaire   +3 more sources