Results 81 to 90 of about 5,420,390 (236)
Efficiency of the Wang-Landau algorithm: a simple test case [PDF]
, 2014 We analyze the efficiency of the Wang-Landau algorithm to sample a multimodal distribution on a prototypical simple test case. We show that the exit time from a metastable state is much smaller for the Wang Landau dynamics than for the original standard ...
Fort, G. +4 more
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Statistics in Medicine, Volume 45, Issue 10-12, May 2026.ABSTRACT With the advent of high‐throughput techniques, multi‐omics data and various clinical outcomes have been collected for a range of diseases. Multi‐omics data play a crucial role in uncovering complex biological processes, yet simultaneous representation learning of such high‐dimensional, heterogeneous multi‐modality data along with clinical ...
Qiyiwen Zhang +4 more
wiley +1 more source
Applied Sciences, 2019 Near-infrared spectroscopy (NIRS) including diffuse optical tomography is an imaging modality which makes use of diffuse light propagation in random media.
Yu Jiang +3 more
doaj +1 more source
Dimension-Independent MCMC Sampling for Inverse Problems with Non-Gaussian Priors [PDF]
, 2014 The computational complexity of MCMC methods for the exploration of complex probability measures is a challenging and important problem. A challenge of particular importance arises in Bayesian inverse problems where the target distribution may be ...
Vollmer, Sebastian J.
core
, 2015 We consider the random walk Metropolis algorithm on $\mathbb{R}^n$ with Gaussian proposals, and when the target probability measure is the $n$-fold product of a one-dimensional law. In the limit $n\to\infty$, it is well known (see [Ann. Appl. Probab.
Jourdain, Benjamin +2 more
core +3 more sources
A Hierarchical Bayesian Model for the Global Holocene Geomagnetic Field
Journal of Geophysical Research: Solid Earth, Volume 131, Issue 5, May 2026.Abstract We present a hierarchical Bayesian model of the global geomagnetic field for the last 8,000 years. The model is built solely with thermoremanent records, which include archeomagnetic and volcanic data. Building upon previous work, this model includes fewer approximations and treats model hyperparameters as probabilistic variables.
M. A. Schanner, S. Panovska, M. Korte
wiley +1 more source
, 2016 U ovom radu govorimo o MCMC algoritmima i ilustriramo njihovu primjenu u bayesovskoj statistici. MCMC algoritmi služe za simuliranje uzorka iz distribucije s gustoćom f koju moramo znati samo do na konstantu.
Bartoš, Elio
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Lateral Variations in Lunar Crustal Thickness Inferred From Apollo Seismic and GRAIL Gravity Data
Journal of Geophysical Research: Planets, Volume 131, Issue 5, May 2026.Abstract The internal structure of the Moon is key to understanding its formation, evolution, and bulk composition. In particular, determining the structure of the crust–mantle interface (Moho), including its lateral variations, is of significant importance, but current knowledge is still insufficient to fully constrain it.
Xiang Zhang +7 more
wiley +1 more source
Quantum self-learning Monte Carlo and quantum-inspired Fourier transform sampler
Physical Review Research, 2020 The self-learning metropolis-Hastings algorithm is a powerful Monte Carlo method that, with the help of machine learning, adaptively generates an easy-to-sample probability distribution for approximating a given hard-to-sample distribution.
Katsuhiro Endo +3 more
doaj +1 more source
Exact active subspace Metropolis-Hastings, with applications to the Lorenz-96 system [PDF]
, 2017 We consider the application of active subspaces to inform a Metropolis-Hastings algorithm, thereby aggressively reducing the computational dimension of the sampling problem. We show that the original formulation, as proposed by Constantine, Kent, and Bui-
Constantine, Paul G. +2 more
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