Results 71 to 80 of about 29,218,928 (220)
Adaptive Gibbs samplers and related MCMC methods [PDF]
The Annals of Applied Probability, 2013 Published in at http://dx.doi.org/10.1214/11-AAP806 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org).
Łatuszyński, Krzysztof +2 more
openaire +3 more sources
A comparison of Bayesian and Fourier methods for frequency determination in asteroseismology
, 2010 Bayesian methods are becoming more widely used in asteroseismic analysis. In particular, they are being used to determine oscillation frequencies, which are also commonly found by Fourier analysis.
Bedding, Timothy R. +4 more
core +1 more source
Data Science Journal, 2017 Incomplete data are ubiquitous in social sciences; as a consequence, available data are inefficient (ineffective) and often biased. In the literature, multiple imputation is known to be the standard method to handle missing data.
Masayoshi Takahashi
semanticscholar +1 more source
Remote SensingA full-waveform inversion (FWI) of ground-penetrating radar (GPR) data can be used to effectively obtain the parameters of a shallow subsurface. Introducing the Markov chain Monte Carlo (MCMC) algorithm into the FWI can reduce the dependence on the ...
Shengchao Wang, Xiangbo Gong, Liguo Han
doaj +1 more source
Earth and Space Science, 2021 The Markov chain Monte Carlo (MCMC) algorithm has been used for retrieving ice cloud microphysical properties in this paper. The retrieval data include effective radius (re), ice water content (IWC), number density (N0), and distribution width parameter (
Xia Ding +3 more
doaj +1 more source
, 2018 Bayesian variable selection regression (BVSR) is able to jointly analyze genome-wide genetic datasets, but the slow computation via Markov chain Monte Carlo (MCMC) hampered its wide-spread usage.
Guan, Yongtao, Zhou, Quan
core +1 more source
Reconstructing Probability Distributions with Gaussian Processes
, 2019 Modern cosmological analyses constrain physical parameters using Markov Chain Monte Carlo (MCMC) or similar sampling techniques. Oftentimes, these techniques are computationally expensive to run and require up to thousands of CPU hours to complete.
McClintock, Thomas, Rozo, Eduardo
core +1 more source
Consistency of Markov chain quasi-Monte Carlo on continuous state spaces
, 2009 The random numbers driving Markov chain Monte Carlo (MCMC) simulation are usually modeled as independent U(0,1) random variables. Tribble [Markov chain Monte Carlo algorithms using completely uniformly distributed driving sequences (2007) Stanford Univ.]
Chen, S., Dick, J., Owen, A. B.
core +5 more sources
Journal of Advances in Modeling Earth SystemsSoil biogeochemical models (SBMs) represent soil variables and their responses to global change. Data assimilation approaches help determine whether SBMs accurately represent soil processes consistent with soil pool and flux measurements.
H. W. Xie +4 more
doaj +1 more source
Markov-chain Monte Carlo method enhanced by a quantum alternating operator ansatz
Physical Review ResearchQuantum computation is expected to accelerate certain computational tasks over classical counterparts. Its most primitive advantage is its ability to sample from classically intractable probability distributions.
Yuichiro Nakano +3 more
doaj +1 more source