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Estimation Of The Number Of Sources And Their Locations In Colored Noise Using Reversible Jump Mcmc
2012
Zenodo
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Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 2012 ...
ESTIMATION OF NUMBER OF SOURCES USING REVERSIBLE JUMP MCMC
Reversible jump MCMC In this section, the reversible jump MCMC method [3] is introduced in the joint estimation frame work described in the ...
Asano and H.Asoh, "Joint estimation of sound source location and noise covariance in spatially colored noise," in Proc. Eusipco 2011, 2011. Fig. 1 . 1 Variation of θ (p) j andN j . ...
doi:10.5281/zenodo.52586
fatcat:rqzw7lkoufg4hbk4zwuclyr37a
Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC
2008
NeuroImage
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The usefulness and feasibility of the method are verified through testing with both simulated and empirical data. ...
A number of brain imaging techniques have been developed in order to investigate brain function and to develop diagnostic tools for various brain disorders. ...
We adopted the reversible-jump MCMC combining classical Metropolis moves with reversible jump (RJ) moves and allowing movement between different parameter spaces that satisfies the detailed balance criterion ...
doi:10.1016/j.neuroimage.2007.12.029
pmid:18314351
pmcid:PMC2929566
fatcat:pusjqwqdhbfe5edcoogrziod3e
Automatic fMRI-guided MEG multidipole localization for visual responses
2009
Human Brain Mapping
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Previously, we introduced the use of individual cortical location and orientation constraints in the spatiotemporal Bayesian dipole analysis setting proposed by Jun et al. ([2005]; Neuroimage 28:84-98) ...
We further demonstrate, using an identical visual stimulation paradigm in both fMRI and MEG, the usefulness of this type of automated approach when investigating activation patterns with several spatially ...
ACKNOWLEDGMENTS The authors wish to thank M.D. Marja Balk for assistance in gathering the functional magnetic resonance imaging data. ...
doi:10.1002/hbm.20570
pmid:18465749
fatcat:3o72hithqnaqvcboxy3x6kykce
Bayesian inverse analysis of neuromagnetic data using cortically constrained multiple dipoles
2007
Human Brain Mapping
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To overcome the difficulties with the present model, we propose the use of loose orientation constraints and combined model of prelocalization utilizing the hierarchical minimum-norm estimate and multiple ...
To enable efficient Markov chain Monte Carlo sampling of the dipole locations, we adopted a parametrization of the source space surfaces with two continuous variables (i.e., spherical angle coordinates ...
Reversible jump Markov chain Monte Carlo Reversible jump MCMC [Green, 1995] allows jumps between models having different dimensional parameter spaces. ...
doi:10.1002/hbm.20334
pmid:17370346
fatcat:grquxtfpmrgntkwwrvsgkfbhyi
Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
2015
Physical Review D
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The Advanced LIGO and Advanced Virgo gravitational wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the first detections. ...
The gravitational waveforms emitted directly encode information about the sources, including the masses and spins of the compact objects. ...
ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the LIGO-Virgo Collaboration in the development of the LALInference toolkit, including internal review of the codes and results. ...
doi:10.1103/physrevd.91.042003
fatcat:trkjw5ck3jes7d3dazp24cmpzq
Bayesian approach to the study of white dwarf binaries in LISA data: The application of a reversible jump Markov chain Monte Carlo method
2009
Physical Review D
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2009 pre-print arXiv:0907.2198v1 fatcat:rlhmwemuvnfvrnltrge46pmqiaOther Versions
colored instrumental noise and additional foreground and background noise successfully in a global fitting approach. ...
) for a given mixture of signals "out of the box", handling the total number of signals as an additional unknown parameter beside the unknown parameters of each individual source and the noise floor. ...
Acknowledgments We acknowledge major contributions to research and paper editing by Alberto Vecchio. JV is supported by the UK Science and Technology Facilities Council. ...
doi:10.1103/physrevd.80.064032
fatcat:as5zq77evnan3hkfvey44saswa
Illumination aware MCMC Particle Filter for long-term outdoor multi-object simultaneous tracking and classification
2009
2009 IEEE 12th International Conference on Computer Vision
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As a first contribution, we propose in this paper to jointly track the light source within the Particle Filter, considering it as an additionnal object. ...
This paper addresses real-time automatic visual tracking, labeling and classification of a variable number of objects such as pedestrians or/and vehicles, under timevarying illumination conditions. ...
For that purpose, the sampling step is operated by a RJ MCMC sampler (Reversible Jump Markov Chain Monte Carlo) [2] , which can sample over a variable dimension state space, as the number of visible objects ...
doi:10.1109/iccv.2009.5459367
dblp:conf/iccv/BardetCR09
fatcat:6gfsesfdtzgy7ak5gnmc5q2gki
Global Analysis of the Gravitational Wave Signal from Galactic Binaries
[article]
2020
arXiv
pre-print
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The unresolved remainder will be the main source of "noise" between 1-3 milli-Hertz. ...
Typical galactic binaries are millions of years from merger, and consequently their signals will persist for the the duration of the LISA mission. ...
We also thank the LISA Consortium's LDC working group for curating and supporting the simulated data used in this study. ...
arXiv:2004.08464v1
fatcat:crybqbzwczdndi7evejatpo3ne
LISA data analysis using Markov chain Monte Carlo methods
2005
Physical Review D
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2005 pre-print arXiv:gr-qc/0506059v1 fatcat:udxh5d3yqvfqzalwogmyaj72d4
We find that the MCMC approach is able to correctly identify the number of signals present, extract the source parameters, and return error estimates consistent with Fisher information matrix predictions ...
Here we present the first application of MCMC methods to simulated LISA data and demonstrate the great potential of the MCMC approach. ...
The popular Delayed Rejection Method [48] and Reversible Jump Method [49] are examples of adaptive MCMC algorithms. ...
doi:10.1103/physrevd.72.043005
fatcat:irm4be6c7nahdmwtp6nkwt4dpe
On the Potential of 3D Transdimensional Surface Wave Tomography for Geothermal Prospecting of the Reykjanes Peninsula
2021
Remote Sensing
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The algorithm is rooted in a Bayesian framework using Markov chains with reversible jumps, and is referred to as transdimensional tomography. ...
In this study, we investigate the potential of this algorithm for the purpose of recovering the three-dimensional surface-wave-velocity structure from ambient noise recorded on and around the Reykjanes ...
The reverse jumps allow for a variable number of Voronoi cells, hence a variable number of parameters. ...
doi:10.3390/rs13234929
fatcat:vcvcubpaqbbwpa7q3qjd35xgu4
Joint Bayesian decomposition of a spectroscopic signal sequence with RJMCMC
2012
2012 IEEE Statistical Signal Processing Workshop (SSP)
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The main contribution concerns the estimation of the peak number using the reversible jump MCMC algorithm. We show the accuracy of this approach on synthetic and real data. ...
This article presents a method for decomposing a sequence of spectroscopic signals into a sum of peaks whose centers, amplitudes and widths are estimated. ...
So we extend this model by considering the peak number as a new random variable (section 2), and we use the reversible jump MCMC (RJMCMC) algorithm [8, 11] which handles problems with unknown dimension ...
doi:10.1109/ssp.2012.6319674
dblp:conf/ssp/MazetFMGPM12
fatcat:jnddwflqyrfx3ijmrinwivqjm4
Quantifying Registration Uncertainty With Sparse Bayesian Modelling
2017
IEEE Transactions on Medical Imaging
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We implement an (asymptotically) exact inference scheme based on reversible jump Markov Chain Monte Carlo (MCMC) sampling to characterize the posterior distribution of the transformation and compare the ...
predictions of the VB and MCMC based methods. ...
[15] ) and reversible jump MCMC [10] . ...
doi:10.1109/tmi.2016.2623608
pmid:27831863
fatcat:ovykhsxc3fcqnaz55ln6csyxly
Imaging the subsurface using induced seismicity and ambient noise: 3D Tomographic Monte Carlo joint inversion of earthquake body wave travel times and surface wave dispersion
2020
Geophysical Journal International
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The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. ...
To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. ...
The data used in this study can be accessed from British Geological Survey (https://earthquakes. bgs.ac.uk/monitoring/data_archive.html). ...
doi:10.1093/gji/ggaa230
fatcat:x7hkvilfvfaerbenkh6c4dztty
Robust and accelerated Bayesian inversion of marine controlled-source electromagnetic data using parallel tempering
2013
Geophysics
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In this approach, a model likelihood function based on knowledge of the data noise statistics is used to sample the posterior model distribution, which conveys information on the resolvability of the model ...
We tested this approach using a transdimensional algorithm, where the number of model parameters as well as the parameters themselves were treated as unknowns during the inversion. ...
Gaussian noise of 2% was added to the modeled data, and a standard source normalized amplitude of 10 −15 V∕Am 2 was used as the noise floor. ...
doi:10.1190/geo2013-0128.1
fatcat:l4jlw2wx4jg3ji2cuqnvrmujd4
A Bayesian Lasso via reversible-jump MCMC
2011
Signal Processing
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As an extension of the proposed RJ-MCMC framework, we also develop an MCMC-based algorithm for the Binomial-Gaussian prior model and illustrate its improved performance over the non-Bayesian estimate via ...
Many variable selection techniques have been proposed in the context of linear regression, and the Lasso model is probably one of the most popular penalized regression techniques. ...
Acknowledgments We thank two anonymous reviewers for their constructive suggestion that significantly improved an earlier version of the paper. ...
doi:10.1016/j.sigpro.2011.02.014
fatcat:fx3osa2u4zcrljs76r3axwzw6u
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