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Randomized reduced forward models for efficient Metropolis–Hastings MCMC, with application to subsurface fluid flow and capacitance tomography
Bayesian modelling and computational inference by Markov chain Monte Carlo (MCMC) is a principled framework for large-scale uncertainty...
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Batch Size Selection for Variance Estimators in MCMC
We consider batch size selection for a general class of multivariate batch means variance estimators, which are computationally viable for...
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An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization
We propose an adaptively weighted stochastic gradient Langevin dynamics (AWSGLD) algorithm for Bayesian learning of big data problems. The proposed...
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Research on Economic Growth Based on MCMC Principal Component Regression Analysis
The MCMC algorithm is integrated into the principal component regression analysis model, and a new principal component regression analysis method is...
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Intelligent Cartoon Image Generation Based on Text Analysis and MCMC Algorithm
Intelligent cartoon image generation is to generate an image corresponding to a given text through a certain pattern. However, the absent of...
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Permeability prediction of soft clay based on digital models reconstructed by an improved Markov chain Monte Carlo method
3D digital models can facilitate the understanding of micro- and nanoscale pore structures and enhance the permeability prediction accuracy of...
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Markov Chain Monte Carlo Methods
The Markov Chain Monte Carlo (MCMC) methods based on the Bayes theorem are used when an a posteriori distribution does not have a tractable form and...
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Accelerating MCMC by Rare Intermittent Resets
We propose a scheme for accelerating Markov Chain Monte Carlo by introducing random resets that become increasingly rare in a precise sense. We show...
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MCMC inversion of the transient and steady-state creep flow law parameters of dunite under dry and wet conditions
The rheology of the upper mantle impacts a variety of geodynamic processes, including postseismic deformation following great earthquakes and...
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Comparing Maximum Likelihood to Markov Chain Monte Carlo Estimation of the Multivariate Social Relations Model
The social relations model (SRM) is a linear random-effects model applied to dyadic data within social networks (i.e., round-robin data). Such data...
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Estimating red noise in quasi-periodic signals with MCMC-based Bayesian
Multi-parameter Bayesian inferences based on Markov chain Monte Carlo (MCMC) samples have been widely used to estimate red noise in solar...
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Birth-Death MCMC Approach for Multivariate Beta Mixture Models in Medical Applications
Lately, data mining tools have received significant attention because of their capability in modeling and analyzing collected data in various fields...
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Reservoir Capacity Planning Using Stochastic Multiobjective Programming Integrated with MCMC Technique
Determining the location and size of new reservoirs requires a risk-informed decision approach. The scope of risk management, which has been...
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MCMC-Based Probabilistic Damage Characterization for Plate Structures Using Responses at Vibration Nodes
Structural Health Monitoring (SHM) has brought various benefits into the industry, such as in economic, life-safety and lightweight design aspects....
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Evaluating Mixture Solution™— rapid and non-MCMC probabilistic mixture analysis
We compare DNA mixture analysis via DNAˑVIEW® Mixture Solution™ and the current combined probability of inclusion (CPI) method of the South African...
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Markov Chain Monte Carlo Cubature Particle Filter for Unbalanced Distribution System State Estimation
In order to overcome the inaccurate proposal distribution along with the particle degeneracy problem of particle filter (PF), this paper proposed a...
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Variational Bayesian analysis of survival data using a log-logistic accelerated failure time model
The log-logistic regression model is one of the most commonly used accelerated failure time (AFT) models in survival analysis, for which statistical...
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Bayesian Estimation of Marshall Olkin Extended Inverse Weibull Distribution Using MCMC Approach
In this paper, we invoke a new prospective to discuss the estimation of a three-parameter Marshall Olkin extended inverse Weibull distribution based...
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Convergence Analysis of MCMC
One of the major issues that many practitioners run into when using MCMC is the slow convergence rate. While many MCMC methods have been shown to...
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A Variational Bayes Approach to Factor Analysis
Factor analysis models are useful dimensionality-reduction techniques for the covariance of observed data. A Bayesian approach to inference for these...