On the asymptotic and approximate distributions of the product of an inverse Wishart matrix and a Gaussian vector [PDF]
Theory of Probability and Mathematical Statistics, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kotsiuba, I., Mazur, Stepan
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Anti-Clutter Gaussian Inverse Wishart PHD Filter for Extended Target Tracking [PDF]
Sensors, 2019 The extended target Gaussian inverse Wishart probability hypothesis density (ET-GIW-PHD) filter overestimates the number of targets under high clutter density. The reason for this is that the source of measurements cannot be determined correctly if only the number of measurements is used. To address this problem, we proposed an anti-clutter filter with
Yuan Huang 0006 +3 more
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Parametric Bayesian Estimation of Differential Entropy and Relative Entropy
Entropy, 2010 Given iid samples drawn from a distribution with known parametric form, we propose the minimization of expected Bregman divergence to form Bayesian estimates of differential entropy and relative entropy, and derive such estimators for the uniform ...
Maya Gupta, Santosh Srivastava
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Probabilistic Clustering Using Multivariate Growth Mixture Model in Clinical Settings-A Scleroderma Example. [PDF]
Stat MedABSTRACT Background Scleroderma (systemic sclerosis; SSc) is a chronic autoimmune disease known for wide heterogeneity in patients' disease progression in multiple organ systems. Our goal is to guide clinical care by real‐time classification of patients into clinically interpretable subpopulations based on their baseline characteristics and the ...
Kim JS +5 more
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On the asymptotic and approximate distributions of the product of an inverse Wishart matrix and a Gaussian vector [PDF]
, 2015 In this paper we study the distribution of the product of an inverse Wishart random matrix and a Gaussian random vector. We derive its asymptotic distribution as well as its approximate density function formula which is based on the Gaussian integral and the third order Taylor expansion.
Kotsiuba, Igor +2 more
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Spatial joint modelling of multivariate longitudinal outcomes and cure proportion using latent Gaussian model with application to dataset on HIV/AIDS patients [PDF]
Scientific ReportsSurvival analysis has seen in recent times more of joint modelling of longitudinal and survival data using approximate hierarchical Bayesian method. This study modelled jointly multivariate and cure proportion with spatial variation using latent Gaussian
Aniefiok Henry Ekong +3 more
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Robust adaptive multi‐target tracking with unknown measurement and process noise covariance matrices
IET Radar, Sonar & Navigation, 2022 A robust adaptive probability hypothesis density (PHD) filter is proposed to address the degradation of PHD performance due to an unknown process noise and measurement noise covariance matrix.
Peng Gu, Zhongliang Jing, Liangbin Wu
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IEEE Access, 2023 We present a nonparametric Bayesian hierarchical (NBH) model and develop a variational approximation (VA) algorithm for the curve fitting of the functional radiation response data.
Kwang-Woo Jung +6 more
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Remote Sensing, 2023 A novel Student’s t-based robust Poisson multi-Bernoulli mixture (PMBM) filter is proposed to effectively perform multi-target tracking under heavy-tailed process and measurement noises.
Jiangbo Zhu, Weixin Xie, Zongxiang Liu
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Poisson multi‐Bernoulli mixture filters with coloured measurement noise
IET Radar, Sonar & Navigation, 2022 To solve multitarget tracking (MTT) problems with coloured measurement noise, this study proposes a Poisson multi‐Bernoulli mixture filter with coloured measurement noise (PMBM‐CMN) and a robust PMBM‐CMN filter.
Wenjuan Li +4 more
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