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Spatial Signal Detection Using Continuous Shrinkage Priors

An-Ting Jhuang, Montserrat Fuentes, Jacob L. Jones, Giovanni Esteves, Chris M. Fancher, Marschall Furman, Brian J. Reich
2018 Technometrics  
Motivated by the problem of detecting changes in two-dimensional X-ray diffraction data, we propose a Bayesian spatial model for sparse signal detection in image data.  ...  We show that the spatial prior places mass on nearby locations simultaneously being zero, and also allows for nearby locations to simultaneously be large signals.  ...  Therefore, we analyze these data using sparse spatial signal detection methods.  ... 
doi:10.1080/00401706.2018.1546622 pmid:31723308 pmcid:PMC6853616 fatcat:hmcbvaizybbxxn2iadhlvbg5wy

Bayesian Variable Selection for Cox Regression Model with Spatially Varying Coefficients with Applications to Louisiana Respiratory Cancer Data [article]

Jinjian Mu, Qingyang Liu, Lynn Kuo, Guanyu Hu
2020 arXiv   pre-print
We propose a Bayesian hierarchical model which incorporates a horseshoe prior for sparsity and a point mass mixture prior to determine whether a regression coefficient is spatially varying.  ...  An efficient two-stage computational method is used for posterior inference and variable selection.  ...  signal with 100 replications for each β β k β k being selected β k detected as spatially varying β k β k being selected β k detected as spatially varying β 1 1 1 β 11 92 13 β 2  ... 
arXiv:2008.00615v1 fatcat:fzppakmmerculpfunez5cygy3a

In Search of Optimal Objective Priors for Model Selection and Estimation [chapter]

Jyotishka Datta, Jayanta Ghosh
2015 Current Trends in Bayesian Methodology with Applications  
Continuous Shrinkage Priors".  ...  INVITED TALKS • December, 2015: "Shrinkage Priors for High-Dimensional Sparse Discrete and Continuous Data", Ninth International Triennial Calcutta Symposium, Kolkata, India. • May,2015: "Multiscale Bayesian  ... 
doi:10.1201/b18502-12 fatcat:tp6tc3zmgzeyjkaqiq22s3uqy4

Iterative Shrinkage Operator for Direction of Arrival Estimation

Yousaf M. Rind
2016 International Journal of Information Engineering and Electronic Business  
The comparison is performed using extensive numerical simulations.  ...  In this correspondence we present the application of iterative shrinkage (IS) operator to the DOA estimation task.  ...  Spatial spectrum is a continuous function. Discretisation of spatial domain lead to grid errors [24] .  ... 
doi:10.5815/ijieeb.2016.05.04 fatcat:s6ath7blvrb5dox5x6l3vfvhli

Neural shrinkage for wavelet-based SAR despeckling [article]

Mario Mastriani, Alberto E. Giraldez
2016 arXiv   pre-print
The wavelet shrinkage denoising approach is able to maintain local regularity of a signal while suppressing noise.  ...  In this paper, a new type of Neural Shrinkage (NS) is presented with a new class of shrinkage architecture for speckle reduction in Synthetic Aperture Radar (SAR) images.  ...  In [34] , the prior probability function is assumed as a piecewise continuous potential function with two constant parts and a linear transition around a predefined threshold.  ... 
arXiv:1608.00279v1 fatcat:qjq2tpnwjbejli7ssd4vmedv7y

Denoising of Digital Images using Consolidation of Edges and Separable Wavelet Transform

Bhumika A., Bhagyashree V., K. M.
2017 International Journal of Computer Applications  
A Noisy image is initially preprocessed using the proposed local edge profile detection and subsequent edge preserving filtering in spatial domain followed further by the modified threshold bivariate shrinkage  ...  We present a novel approach to image denoising using edge profile detection and edge preservation in spatial domain in presence of zero mean additive Gaussian noise.  ...  This shrinkage function requires the prior knowledge of the noise variance and the signal variance for each wavelet coefficient as mentioned in section 2.  ... 
doi:10.5120/ijca2017914512 fatcat:46wjjdn2qbbzjatu4xxewpi5aq

Bayesian Tensor Response Regression with an Application to Brain Activation Studies

Rajarshi Guhaniyogi, Daniel Spencer
2021 Bayesian Analysis  
To estimate model parameters with proper cell specific shrinkage, we propose a novel multiway stick breaking shrinkage prior distribution on tensor structured regression coefficients, enabling identification  ...  The major novelty of this article lies in the theoretical study of the contraction properties for the proposed shrinkage prior in the tensor response regression when the number of cells grows faster than  ...  logistic regression models, respectively, depending on the concentration and tail properties of the density of the continuous shrinkage prior.  ... 
doi:10.1214/21-ba1280 fatcat:q5ynifbdzbhuzi2hsvivgc3gae

Image Denoising Based on Wavelet Analysis for Satellite Imagery [chapter]

Parthasarathy Subashini, Marimuthu Krishnaveni
2012 Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology  
Classical image enhancement techniques consider the use of spatial-invariant operators either in the spatial or in the fourier domain.  ...  The procedure does not require any assumptions about the nature of the signal, permits discontinuities and spatial variation in the signal, and exploits the spatially adaptive multiresolution features  ... 
doi:10.5772/36140 fatcat:peb56kehereajkjpp3ieixw3xy

Combined spatial and temporal domain wavelet shrinkage algorithm for video denoising

E.J. Bal, Y.F. Zheng, R.L. Ewing
2006 IEEE transactions on circuits and systems for video technology (Print)  
The spatial-domain denoising technique is a selective wavelet shrinkage method which uses a two-threshold criteria to exploit the geometry of the wavelet subbands of each video frame, and each frame of  ...  A combined spatial-and temporal-domain wavelet shrinkage algorithm for video denoising is presented in this paper.  ...  Spatial averaging is used to remove the noise inherent in the signal, and the temporal standard deviation is used to detect the amount of activity in the temporal domain.  ... 
doi:10.1109/tcsvt.2005.857816 fatcat:uolvlcal6zf6hii6sguyizuqay

Spectrum sensing with spatial signatures in the presence of noise uncertainty and shadowing

Sadiq Ali, Gonzalo Seco-Granados, José A López-Salcedo
2013 EURASIP Journal on Wireless Communications and Networking  
The main contribution of this work is the derivation of a cognitive detector based on the generalized likelihood ratio test and the use of spatial signatures, a novel concept that allows the detector to  ...  capture the spatial correlation inherently embedded in measurements coming from neighboring sensors.  ...  Here again, we can see that using spatial signatures, the detection performance also improves compared to the unstructured GLRT UG (X), with or without shrinkage.  ... 
doi:10.1186/1687-1499-2013-150 fatcat:oxmfx7vd55f6rhqfmh566bxibq

Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models

Michael Pfarrhofer, Philipp Piribauer
2019 Spatial Statistics  
This article introduces two absolutely continuous global-local shrinkage priors to enable stochastic variable selection in the context of high-dimensional matrix exponential spatial specifications.  ...  The proposed shrinkage priors can be implemented using Markov chain Monte Carlo methods in a flexible and efficient way.  ...  In the following, we discuss two alternatives of continuous global-local shrinkage, the Normal-Gamma and the Dirichlet-Laplace shrinkage prior.  ... 
doi:10.1016/j.spasta.2018.10.004 fatcat:wluk5wvu6jcynjb63n6wqojsjy

plusTipTracker: Quantitative image analysis software for the measurement of microtubule dynamics

Kathryn T. Applegate, Sebastien Besson, Alexandre Matov, Maria H. Bagonis, Khuloud Jaqaman, Gaudenz Danuser
2011 Journal of Structural Biology  
The algorithm underlying the reconstruction of full MT trajectories relies on the spatially and temporally global tracking framework described in (Jaqaman et al., 2008) .  ...  Although +TIPs mark only phases of MT growth, the plusTipTracker software allows inference of additional MT dynamics, including phases of pause and shrinkage, by linking collinear, sequential growth tracks  ...  Acknowledgments We thank Ken Myers (NIH/NHLBI) for providing EB3 movies, Alexis Lomakin for the EB3 movie in Fig. 4 , and Torsten Wittmann (UCSF) for providing the two-color EB1/MT movie used for validation  ... 
doi:10.1016/j.jsb.2011.07.009 pmid:21821130 pmcid:PMC3298692 fatcat:bkirwf6l7bfgbifm6f5sargoea

A Geometrical Wavelet Shrinkage Approach For Image Denoising

Bruno Huysmans
2006 Zenodo  
In early methods only the coefficient magnitude was used to predict whether a coefficient represents useful signal or mainly noise [2, 11] .  ...  The methods of [7] , [6] and [10] combine the intra-and interscale dependencies with a bilevel Markov Random Field (MRF) model, which encodes the prior knowledge about the spatial clustering of wavelet  ... 
doi:10.5281/zenodo.53399 fatcat:jsvcpz3op5hqdcmfnxux2oyfau

On Global-Local Shrinkage Priors for Count Data

Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
2021 Bayesian Analysis  
Global-local shrinkage priors have been recognized as a useful class of priors that can strongly shrink small signals toward prior means while keeping large signals unshrunk.  ...  Previous contributions on global-local shrinkage priors cannot be readily applied to count data. In this paper, we discuss global-local shrinkage priors for analyzing a sequence of counts.  ...  Most notably, the horseshoe prior (Carvalho et al., 2010) has been proposed to detect sparse signals in high-dimensional continuous observations.  ... 
doi:10.1214/21-ba1263 fatcat:rvmjtwlyfjfjphjgbtwldfxi2a

Spatial shrinkage via the product independent Gaussian process prior [article]

Arkaprava Roy, Brian J. Reich, Joseph Guinness, Russell T. Shinohara, Ana-Maria Staicu
2020 arXiv   pre-print
We study the problem of sparse signal detection on a spatial domain.  ...  The simulation results demonstrate the improvement in estimation using the PING prior over Gaussian process (GP) prior for different image regressions.  ...  Source-code In the spatial-shrinkage.zip, the .tex file is kept. SUPPLEMENTARY MATERIAL  ... 
arXiv:1805.03240v4 fatcat:747d72vtfnfbpbgdmasd47cyqe
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