Zobrazeno 1 - 10
of 41
pro vyhledávání: '"MOORES, MATTHEW T."'
We propose a statistical approach for estimating the mean line width in spectra comprising Lorentzian, Gaussian, or Voigt line shapes. Our approach uses Gaussian processes in two stages to jointly model a spectrum and its Fourier transform. We genera
Externí odkaz:
http://arxiv.org/abs/2404.06338
We propose an approach utilizing gamma-distributed random variables, coupled with log-Gaussian modeling, to generate synthetic datasets suitable for training neural networks. This addresses the challenge of limited real observations in various applic
Externí odkaz:
http://arxiv.org/abs/2310.08055
We propose a statistical model for narrowing line shapes in spectroscopy that are well approximated as linear combinations of Lorentzian or Voigt functions. We introduce a log-Gaussian Cox process to represent the peak locations thereby providing unc
Externí odkaz:
http://arxiv.org/abs/2202.13120
In Bayesian statistics, exploring multimodal posterior distribution poses major challenges for existing techniques such as Markov Chain Monte Carlo (MCMC). These problems are exacerbated in high-dimensional settings where MCMC methods typically rely
Externí odkaz:
http://arxiv.org/abs/2112.12908
Autor:
Cressie, Noel, Moores, Matthew T.
Spatial statistics is an area of study devoted to the statistical analysis of data that have a spatial label associated with them. Geographers often refer to the "location information" associated with the "attribute information," whose study defines
Externí odkaz:
http://arxiv.org/abs/2105.07216
Markov chain Monte Carlo methods for exponential family models with intractable normalizing constant, such as the exchange algorithm, require simulations of the sufficient statistics at every iteration of the Markov chain, which often result in expen
Externí odkaz:
http://arxiv.org/abs/2105.04374
We propose a Bayesian statistical model for analyzing coherent anti-Stokes Raman scattering (CARS) spectra. Our quantitative analysis includes statistical estimation of constituent line-shape parameters, underlying Raman signal, error-corrected CARS
Externí odkaz:
http://arxiv.org/abs/2005.06830
This article surveys computational methods for posterior inference with intractable likelihoods, that is where the likelihood function is unavailable in closed form, or where evaluation of the likelihood is infeasible. We review recent developments i
Externí odkaz:
http://arxiv.org/abs/2004.04620
The inverse temperature parameter of the Potts model governs the strength of spatial cohesion and therefore has a major influence over the resulting model fit. A difficulty arises from the dependence of an intractable normalising constant on the valu
Externí odkaz:
http://arxiv.org/abs/1503.08066
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.