Compressive Sensing via Variational Bayesian Inference under Two Widely Used Priors: Modeling, Comparison and Discussion [PDF]
Entropy, 2023 Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying ...
Mohammad Shekaramiz, Todd K. Moon
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Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects [PDF]
Entropy, 2022 Traditionally, Hawkes processes are used to model time-continuous point processes with history dependence. Here, we propose an extended model where the self-effects are of both excitatory and inhibitory types and follow a Gaussian Process.
Noa Malem-Shinitski +2 more
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Extended Variational Message Passing for Automated Approximate Bayesian Inference
Entropy, 2021 Variational Message Passing (VMP) provides an automatable and efficient algorithmic framework for approximating Bayesian inference in factorized probabilistic models that consist of conjugate exponential family distributions.
Bert de Vries
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Decision, Inference, and Information: Formal Equivalences Under Active Inference [PDF]
EntropyA central challenge in artificial intelligence and cognitive science is identifying a unifying principle that governs inference, learning, and action. Active inference proposes such a principle: the minimization of variational free energy.
Patrick Sweeney +2 more
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Variational Bayesian Compressive Sensing with Equivalent Source Modeling for Sound Field Reconstruction [PDF]
SensorsWhile conventional Bayesian compressive sensing exploits signal sparsity for accurate sound field reconstruction from under-sampled measurements, its practicality is limited by high computational complexity and slow convergence.
Yue Xiao +3 more
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Overview of Research on Bayesian Inference and Parallel Tempering [PDF]
Jisuanji kexue, 2023 Bayesian inference is one of the main problems in statistics.It aims to update the prior knowledge of the probability distribution model based on the observation data.For the posterior probability that cannot be observed or is difficult to directly ...
ZHAN Jin, WANG Xuefei, CHENG Yurong, YUAN 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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A Design Methodology for Fault-Tolerant Neuromorphic Computing Using Bayesian Neural Network
Micromachines, 2023 Memristor crossbar arrays are a promising platform for neuromorphic computing. In practical scenarios, the synapse weights represented by the memristors for the underlying system are subject to process variations, in which the programmed weight when read
Di Gao, Xiaoru Xie, Dongxu Wei
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A primer on Variational Laplace (VL)
NeuroImage, 2023 This article details a scheme for approximate Bayesian inference, which has underpinned thousands of neuroimaging studies since its introduction 15 years ago.
Peter Zeidman, Karl Friston, Thomas Parr
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