Search results for "Statistics::Computation"
showing 10 items of 48 documents
Grapham: Graphical models with adaptive random walk Metropolis algorithms
2008
Recently developed adaptive Markov chain Monte Carlo (MCMC) methods have been applied successfully to many problems in Bayesian statistics. Grapham is a new open source implementation covering several such methods, with emphasis on graphical models for directed acyclic graphs. The implemented algorithms include the seminal Adaptive Metropolis algorithm adjusting the proposal covariance according to the history of the chain and a Metropolis algorithm adjusting the proposal scale based on the observed acceptance probability. Different variants of the algorithms allow one, for example, to use these two algorithms together, employ delayed rejection and adjust several parameters of the algorithm…
Design of the friction stir welding tool using the continuum based FEM model
2006
In friction stir welding (FSW), the welding tool geometry plays a fundamental role in obtaining desirable microstructures in the weld and the heat-affected zones, and consequently improving strength and fatigue resistance of the joint. In this paper, a FSW process with varying pin geometries (cylindrical and conical) and advancing speeds is numerically modeled, and a thermo-mechanically coupled, rigid-viscoplastic, fully 3D FEM analysis able to predict the process variables as well as the material flow pattern and the grain size in the welded joints is performed. The obtained results allow finding optimal tool geometry and advancing speed for improving nugget integrity of aluminum alloys.
Hessian PDF reweighting meets the Bayesian methods
2014
We discuss the Hessian PDF reweighting - a technique intended to estimate the effects that new measurements have on a set of PDFs. The method stems straightforwardly from considering new data in a usual $\chi^2$-fit and it naturally incorporates also non-zero values for the tolerance, $\Delta\chi^2>1$. In comparison to the contemporary Bayesian reweighting techniques, there is no need to generate large ensembles of PDF Monte-Carlo replicas, and the observables need to be evaluated only with the central and the error sets of the original PDFs. In spite of the apparently rather different methodologies, we find that the Hessian and the Bayesian techniques are actually equivalent if the $\Delta…
Sequential Monte Carlo Methods in Random Intercept Models for Longitudinal Data
2017
Longitudinal modelling is common in the field of Biostatistical research. In some studies, it becomes mandatory to update posterior distributions based on new data in order to perform inferential process on-line. In such situations, the use of posterior distribution as the prior distribution in the new application of the Bayes’ theorem is sensible. However, the analytic form of the posterior distribution is not always available and we only have an approximated sample of it, thus making the process “not-so-easy”. Equivalent inferences could be obtained through a Bayesian inferential process based on the set that integrates the old and new data. Nevertheless, this is not always a real alterna…
"Table 30" of "Search for photonic signatures of gauge-mediated supersymmetry in 8 TeV $pp$ collisions with the ATLAS detector"
2015
The total NLO+NLL cross sections with uncertainties for the strong production GGM signal grid for the photon+j analysis.
"Table 2" of "The spin-dependent structure function g1(x) of the deuteron from polarized deep-inelastic muon scattering."
1997
The virtual-photon deuteron cross section asymmetry A1 from the combined SMC data. Statistical errors only.
"Table 6" of "Measurement of D*+/- meson production in jets from pp collisions at sqrt(s) = 7 TeV with the ATLAS detector"
2012
Comparison of the reconstructed jet PT distribution with the reweighted Monte Carlo prediction.
"Table 7" of "Measurement of D*+/- meson production in jets from pp collisions at sqrt(s) = 7 TeV with the ATLAS detector"
2012
Comparison of the reconstructed Z distribution with the reweighted Monte Carlo prediction.
Threshold cointegration and nonlinear adjustment between goods and services inflation in the United States
2006
In this paper, we model the long-run relationship between goods and services inflation for the United States over the period 1968:1–2003:3. Our empirical methodology makes use of recent developments on threshold cointegration that consider the possibility of a nonlinear relationship between the two inflation series. According to our results, the null hypothesis of linear cointegration would be rejected in favor of a two-regime threshold cointegration model. Consequently, we could expect a cointegrating relationship only when the divergence between services inflation and goods inflation is above the threshold point estimate.
Markov chain Monte Carlo importance samplers for Bayesian models with intractable likelihoods
2019
Markov chain Monte Carlo (MCMC) is an approach to parameter inference in Bayesian models that is based on computing ergodic averages formed from a Markov chain targeting the Bayesian posterior probability. We consider the efficient use of an approximation within the Markov chain, with subsequent importance sampling (IS) correction of the Markov chain inexact output, leading to asymptotically exact inference. We detail convergence and central limit theorems for the resulting MCMC-IS estimators. We also consider the case where the approximate Markov chain is pseudo-marginal, requiring unbiased estimators for its approximate marginal target. Convergence results with asymptotic variance formula…