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Optimal Transport for Gaussian Mixture Models [PDF]
IEEE Access, 2019 We introduce an optimal mass transport framework on the space of Gaussian mixture models. These models are widely used in statistical inference. Specifically, we treat the Gaussian mixture models as a submanifold of probability densities equipped with ...
Yongxin Chen +2 more
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Numerical Evaluation of Gaussian Mixture Entropy [PDF]
EntropyWe develop an approximation method for the differential entropy h(X) of a q-component Gaussian mixture in Rn. We provide two examples of approximations using our method denoted by h¯C,mTaylor(X) and h¯CPolyfit(X).
Basheer Joudeh, Boris Škorić
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A novel trajectory learning method for robotic arms based on Gaussian Mixture Model and k-value selection algorithm. [PDF]
PLoS ONEIn the field of robotic arm trajectory imitation learning, Gaussian Mixture Models are widely used for their ability to capture the characteristics of complex trajectories. However, one major challenge in utilizing these models lies in the initialization
Jingnan Yan +4 more
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Gaussian approximation of Gaussian scale mixtures [PDF]
Kybernetika, 2021 For a given positive random variable $V>0$ and a given $Z\sim N(0,1)$ independent of $V$, we compute the scalar $t_0$ such that the distance between $Z\sqrt{V}$ and $Z\sqrt{t_0}$ in the $L^2(\R)$ sense, is minimal. We also consider the same problem in several dimensions when $V$ is a random positive definite matrix.
Gérard Letac, Hélène Massam
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Intrinsically Interpretable Gaussian Mixture Model
Information, 2023 Understanding the reasoning behind a predictive model’s decision is an important and longstanding problem driven by ethical and legal considerations.
Nourah Alangari +3 more
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Machine Learning based on Probabilistic Models Applied to Medical Data: The Case of Prostate Cancer
Journal of Innovation Information Technology and Application, 2023 The growth in the amount of data in companies puts analysts in difficulties when extracting hidden knowledge from data. Several models have emerged that focus on the notion of distances while ignoring the notion of conditional probability density.
Anaclet Tshikutu Bikengela +4 more
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Label GM-PHD Filter Based on Threshold Separation Clustering
Sensors, 2021 Gaussian mixture probability hypothesis density (GM-PHD) filtering based on random finite set (RFS) is an effective method to deal with multi-target tracking (MTT).
Kuiwu Wang, Qin Zhang, Xiaolong Hu
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Deep Gaussian Mixture Ensembles
CoRR, 2023 This work introduces a novel probabilistic deep learning technique called deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification of both epistemic and aleatoric uncertainty. By assuming the data generating process follows that of a Gaussian mixture, DGMEs are capable of approximating complex probability distributions, such as ...
Yousef El-Laham +3 more
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CPAP Adherence Assessment via Gaussian Mixture Modeling of Telemonitored Apnea Therapy
Applied Sciences, 2022 Sleep disorders pose serious cardiovascular threats if not treated effectively. However, adherence to Continuous Positive Airway Pressure (CPAP), the most recommended therapy, is known to be challenging to monitor.
Jose F. Rodrigues +5 more
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Deep Gaussian mixture models [PDF]
Statistics and Computing, 2017 Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers of latent variables, where, at each layer, the variables ...
Cinzia Viroli, Geoffrey J. McLachlan
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