Results 31 to 40 of about 63,287 (274)
Recursive Variational Bayesian Inference to Simultaneous Registration and Fusion
International Journal of Advanced Robotic Systems, 2016 In this paper, we propose a novel simultaneous registration and fusion approach for tracking. This method is based on a recursive Variational Bayesian (RVB) algorithm, which is the online variant of the Variational Bayesian (VB) approach.
Hao Zhu +3 more
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Dynamic causal modelling of COVID-19 and its mitigations
Scientific Reports, 2022 This technical report describes the dynamic causal modelling of mitigated epidemiological outcomes during the COVID-9 coronavirus outbreak in 2020. Dynamic causal modelling is a form of complex system modelling, which uses ‘real world’ timeseries to ...
Karl J. Friston +2 more
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Variational Bayesian image modelling
Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005 We present a variational Bayesian framework for performing inference, density estimation and model selection in a special class of graphical models---Hidden Markov Random Fields (HMRFs). HMRFs are particularly well suited to image modelling and in this paper, we apply them to the problem of image segmentation.
Li Cheng 0001 +3 more
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High-Dimensional Variable Selection for Quantile Regression Based on Variational Bayesian Method
Mathematics, 2023 The quantile regression model is widely used in variable relationship research of moderate sized data, due to its strong robustness and more comprehensive description of response variable characteristics.
Dengluan Dai, Anmin Tang, Jinli Ye
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Variational Inference for Nonlinear Structural Identification [PDF]
Journal of Applied and Computational Mechanics, 2021 Research interest in predictive modeling within the structural engineering community has recently been focused on Bayesian inference methods, with particular emphasis on analytical and sampling approaches. In this study, we explore variational inference,
Alana Lund +2 more
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Sampling the Variational Posterior with Local Refinement
Entropy, 2021 Variational inference is an optimization-based method for approximating the posterior distribution of the parameters in Bayesian probabilistic models. A key challenge of variational inference is to approximate the posterior with a distribution that is ...
Marton Havasi +4 more
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Objective Bayesian Inference in Probit Models with Intrinsic Priors Using Variational Approximations
Entropy, 2020 There is not much literature on objective Bayesian analysis for binary classification problems, especially for intrinsic prior related methods. On the other hand, variational inference methods have been employed to solve classification problems using ...
Ang Li, Luis Pericchi, Kun Wang
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Old Photos Restoration by Using VAE [PDF]
SHS Web of Conferences, 2023 VAE is a generative model that “provides a probabilistic description of observations in potential Spaces”. Put simply, this means that VAE stores potential attributes as probability distributions.
Ma Pingyi, Kuang Haozhe
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Variationally Inferred Sampling through a Refined Bound
Entropy, 2021 In this work, a framework to boost the efficiency of Bayesian inference in probabilistic models is introduced by embedding a Markov chain sampler within a variational posterior approximation.
Víctor Gallego, David Ríos Insua
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Bayesian and variational Bayesian approaches for flows in heterogeneous random media [PDF]
Journal of Computational Physics, 2017 In this paper, we study porous media flows in heterogeneous stochastic media. We propose an efficient forward simulation technique that is tailored for variational Bayesian inversion. As a starting point, the proposed forward simulation technique decomposes the solution into the sum of separable functions (with respect to randomness and the space ...
Keren Yang +3 more
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