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Metropolis algorithm

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Samples generated by the <span class="search-highlight">Metropolis</span> <span class="search-highlight">algorithm</span> plotted in the parameter spac...
Published: 01 December 2007
Figure 8. Samples generated by the Metropolis algorithm plotted in the parameter space. The x axis denotes the sample number. The vertical dotted lines indicate the end of the burn-in period (100 samples).
Journal Article
Journal: Geophysics
Published: 24 February 2012
Geophysics (2012) 77 (2): H19–H31.
...Knud Skou Cordua; Thomas Mejer Hansen; Klaus Mosegaard ABSTRACT We present a general Monte Carlo full-waveform inversion strategy that integrates a priori information described by geostatistical algorithms with Bayesian inverse problem theory. The extended Metropolis algorithm can be used to sample...
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Journal Article
Journal: Geophysics
Published: 21 June 2022
Geophysics (2022) 87 (4): R349–R361.
..., such as the use of a double-difference time-lapse FWI (DDFWI), incorporation of time-domain multisource data, and application of a local-updating target-oriented inversion. However, it incorporates these within a stochastic framework, involving computation of model covariance with an adaptive Metropolis algorithm...
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Journal Article
Journal: Geophysics
Published: 24 August 2012
Geophysics (2012) 77 (5): ID23–ID39.
... signals corresponding to the occurrence of a fracking event in a two-layers system. We perform a stochastic joint inversion of the seismograms and electrograms using the adaptive Metropolis algorithm (AMA) to obtain the posterior probability density functions of the parameters characterizing the seismic...
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Journal Article
Journal: Geophysics
Published: 15 March 2016
Geophysics (2016) 81 (2): E89–E101.
.... The adaptive metropolis algorithm was used to find the proposal distributions of y reproducing the geophysical data and the geophysical information. In other words, we have tried to find a compromise between the a priori geologic information and the geophysical data to get, as end products, an updated geologic...
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Journal Article
Published: 01 February 2015
American Mineralogist (2015) 100 (2-3): 459–465.
... asymptotically to a value of 2.5, in agreement with the results obtained from the simulation of virtual nodules, by means of a diffusion-limited aggregation model based on a Monte Carlo Metropolis algorithm, in which the growth probability at the tips of the nodule is an inverse function of the diffusion...
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Journal Article
Published: 01 February 2013
Vadose Zone Journal (2013) 12 (1): vzj2012.0101.
... moisture estimation using travel-time observations from crosshole ground-penetrating radar experiments. The recently developed Multi-Try Differential Evolution Adaptive Metropolis algorithm with sampling from past states, MT-DREAM (ZS) , was used to infer, as closely and consistently as possible...
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Journal Article
Journal: Geophysics
Published: 25 January 2010
Geophysics (2010) 75 (1): N19–N31.
... on the adaptive Metropolis algorithm, to obtain the posterior probability density functions of the material properties of each geologic unit. This includes the permeability, porosity, electrical conductivity, bulk modulus of the dry porous frame, bulk modulus of the fluid, bulk modulus of the solid phase...
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Journal Article
Published: 01 November 2004
Vadose Zone Journal (2004) 3 (4): 1128–1145.
... permittivity profile along the length of the probe and, therefore, the distribution of water content. The approach is based on the combination of a multisection scatter function model for the TDR measurement system with the shuffled complex evolution Metropolis algorithm (SCEM-UA). This combined approach...
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Distribution of samples for three parameters generated by the <span class="search-highlight">Metropolis</span> al...
Published: 01 December 2007
Figure 10. Distribution of samples for three parameters generated by the Metropolis algorithm. The Gaussian distributions obtained from the asymptotic approximation are added in the figure and fit the histogram well.
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Correlation plot of posterior samples of the model parameters generated by ...
Published: 01 December 2007
Figure 11. Correlation plot of posterior samples of the model parameters generated by the Metropolis algorithm. The most probable values of the parameters are shown as crosses (×). The numbers in the figure are the correlation coefficients of the parameters.
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Schematic workflow. The top row summarizes the stochastic steady-state simu...
Published: 15 August 2019
Figure 3. Schematic workflow. The top row summarizes the stochastic steady-state simulations in SHEMAT-Suite and subsequent application of the Metropolis algorithm. In the bottom row, characteristic scenarios of the posterior temperature distribution are implemented in PetroMod. Deriving
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Schematic representation of the <span class="search-highlight">Metropolis</span>–Hastings     MCMC     <span class="search-highlight">algorithm</span>....
Published: 01 August 2022
Figure 4. Schematic representation of the Metropolis–Hastings MCMC algorithm.
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<span class="search-highlight">Metropolis</span>-Hastings <span class="search-highlight">algorithm</span> using the linear (upper row) and quadratic (l...
Published: 02 May 2011
Figure 8. Metropolis-Hastings algorithm using the linear (upper row) and quadratic (lower row) forward model for contrasts in the three elastic parameters, P-wave impedance (left column), S-wave impedance (middle column), and density (right column).
Journal Article
Published: 01 June 2008
Bulletin of the Seismological Society of America (2008) 98 (3): 1128–1146.
... Monte Carlo ( MCMC ) method. Assuming the current state of a Markov chain, x ( t ) , the Metropolis algorithm generates the next state of the Markov chain, x ( t +1) , by a two-step procedure. The first step generates a candidate state, x ′ , using a proposal probability density, q ( x ′ | x...
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Journal Article
Published: 01 February 2012
Vadose Zone Journal (2012) 11 (1): vzj2011.0082.
... a labeling with the constraint of satisfying Eq. [1] is a combinatorial optimization problem. To investigate different optimization approaches, we initially implemented a deterministic (iterated conditional modes [ICM] algorithm) and a heuristic (Metropolis algorithm) optimization scheme. Because...
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Journal Article
Journal: Geophysics
Published: 17 July 2023
Geophysics (2023) 88 (5): M225–M237.
... (MCMC) probabilistic algorithm incorporating the delayed rejection adaptive Metropolis (DRAM) strategy has been developed to invert the reservoir brittleness index directly. The different brittleness indexes are analyzed and compared by using the constructed rock-physics model of the shale gas...
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Series: Society of Exploration Geophysicists Geophysical Developments Series
Published: 18 December 2024
DOI: 10.1190/1.9781560804048.ch19
EISBN: 9781560804048
... i + 1 = x ~ i + 1 e l s e x i + 1 = x i e n d . Figure 19.6 Metropolis workflow algorithm where samples x i are obtained using the proposal distribution g ~ ( x ) = N ( x , 1 ) centered at x , where...
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Journal Article
Published: 01 December 2007
Bulletin of the Seismological Society of America (2007) 97 (6): 1890–1910.
...Figure 8. Samples generated by the Metropolis algorithm plotted in the parameter space. The x axis denotes the sample number. The vertical dotted lines indicate the end of the burn-in period (100 samples). ...
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Journal Article
Published: 01 February 2003
Vadose Zone Journal (2003) 2 (1): 98–113.
...; Kosugi, 1996 , 1999 ) using the recently developed Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm ( Vrugt et al., 2002b , and unpublished data). Because the SCEM-UA algorithm globally thoroughly exploits the parameter space and therefore explicitly accounts for parameter interdependence...
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