Bayesian Estimations Using MCMC Approach Under Three-Parameter Burr-XII Distribution Based on Unified Hybrid Censored Scheme
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Statistical Theory and Practice"
DOI: 10.1007/s42519-019-0066-3
Abstract: AbstractIn this paper, we discussed the estimation of the unknown parameters in addition to survival and hazard functions for a three-parameter Burr-XII distribution based on unified hybrid censored data. The maximum likelihood and Bayes method… read more here.
Keywords: three parameter; distribution based; mcmc; parameter burr ... See more keywords
Facilitating Bayesian analysis of combustion kinetic models with artificial neural network
Sign Up to like & getrecommendations! Published in 2020 at "Combustion and Flame"
DOI: 10.1016/j.combustflame.2019.11.035
Abstract: Abstract Bayesian analysis provides a framework for the inverse uncertainty quantification (UQ) of combustion kinetic models. As the workhorse of the Bayesian approach, the Markov chain Monte Carlo (MCMC) methods, however, incur a substantial computational… read more here.
Keywords: ann mcmc; combustion; surrogate; bayesian analysis ... See more keywords
Estimating uncertainty in MRF-based image segmentation: A perfect-MCMC approach
Sign Up to like & getrecommendations! Published in 2019 at "Medical image analysis"
DOI: 10.1016/j.media.2019.04.014
Abstract: Typical methods for image segmentation, or labeling, formulate and solve an optimization problem to produce a single optimal solution. For applications in clinical decision support relying on automated medical image segmentation, it is also desirable… read more here.
Keywords: image; uncertainty; image segmentation; mcmc ... See more keywords
Speeding Up MCMC by Efficient Data Subsampling
Sign Up to like & getrecommendations! Published in 2019 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2018.1448827
Abstract: ABSTRACT We propose subsampling Markov chain Monte Carlo (MCMC), an MCMC framework where the likelihood function for n observations is estimated from a random subset of m observations. We introduce a highly efficient unbiased estimator… read more here.
Keywords: speeding mcmc; mcmc efficient; likelihood; mcmc ... See more keywords
The Block-Poisson Estimator for Optimally Tuned Exact Subsampling MCMC
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Computational and Graphical Statistics"
DOI: 10.1080/10618600.2021.1917420
Abstract: Abstract Speeding up Markov chain Monte Carlo (MCMC) for datasets with many observations by data subsampling has recently received considerable attention. A pseudo-marginal MCMC method is proposed that estimates the likelihood by data subsampling using… read more here.
Keywords: block poisson; mcmc; estimator; poisson estimator ... See more keywords
Neural Langevin Dynamical Sampling
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2972611
Abstract: Sampling technique is one of the asymptotically unbiased estimation approaches for inference in Bayesian probabilistic models. Markov chain Monte Carlo (MCMC) is a kind of sampling methods, which is widely used in the inference of… read more here.
Keywords: dynamical sampling; neural langevin; mcmc; langevin dynamical ... See more keywords
Learning Deep Generative Models With Doubly Stochastic Gradient MCMC
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2017.2688499
Abstract: Deep generative models (DGMs), which are often organized in a hierarchical manner, provide a principled framework of capturing the underlying causal factors of data. Recent work on DGMs focussed on the development of efficient and… read more here.
Keywords: doubly stochastic; mcmc; gradient mcmc; generative models ... See more keywords
Stratified Markov Chain Monte Carlo Light Transport
Sign Up to like & getrecommendations! Published in 2020 at "Computer Graphics Forum"
DOI: 10.1111/cgf.13935
Abstract: Markov chain Monte Carlo (MCMC) sampling is a powerful approach to generate samples from an arbitrary distribution. The application to light transport simulation allows us to efficiently handle complex light transport such as highly occluded… read more here.
Keywords: mcmc; markov chain; light transport;
Direct inversion for shale brittleness index using DRAM-MCMC method
Sign Up to like & getrecommendations! Published in 2023 at "GEOPHYSICS"
DOI: 10.1190/geo2022-0567.1
Abstract: The reservoir brittleness index can characterize the relative brittleness of shale oil and gas reservoirs, which provides guidance for hydraulic fracturing in the later stage of oil and gas exploration and development. It is a… read more here.
Keywords: mcmc; index; gas; inversion ... See more keywords
Convergence rates of two-component MCMC samplers
Sign Up to like & getrecommendations! Published in 2022 at "Bernoulli"
DOI: 10.3150/21-bej1369
Abstract: Component-wise MCMC algorithms, including Gibbs and conditional Metropolis-Hastings samplers, are commonly used for sampling from multivariate probability distributions. A long-standing question regarding Gibbs algorithms is whether a deterministic-scan (systematic-scan) sampler converges faster than its random-scan… read more here.
Keywords: metropolis hastings; conditional metropolis; convergence rates; two component ... See more keywords
Automated model calibration with parallel MCMC: Applications for a cardiovascular system model
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Physiology"
DOI: 10.3389/fphys.2022.1018134
Abstract: Computational physiological models continue to increase in complexity, however, the task of efficiently calibrating the model to available clinical data remains a significant challenge. One part of this challenge is associated with long calibration times,… read more here.
Keywords: cardiovascular system; mcmc; calibration; model ... See more keywords