Direct fixed-weight solver for free-support Wasserstein medians relocates atoms using OT barycentric projections and inverse-distance weights, achieving monotone descent on smoothed objectives with fewer subproblems than nested Weiszfeld baselines.
hub
Rank-normalization, folding, and localization: An improved R for assessing convergence of MCMC
22 Pith papers cite this work. Polarity classification is still indexing.
22
Pith papers citing it
hub tools
citation-role summary
background 2
citation-polarity summary
fields
stat.ME 7 math.ST 3 stat.AP 3 stat.CO 2 astro-ph.CO 1 cs.AI 1 cs.HC 1 hep-ex 1 physics.geo-ph 1 q-bio.PE 1roles
background 2polarities
background 2representative citing papers
Uncorrected Gaussian residual penalties in full-space sampling converge after marginalization to the graph-lifted reduced posterior multiplied by the inverse absolute determinant of the state Jacobian, requiring explicit determinant corrections for equivalence.
Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
Applies value-of-information decision analysis to quantify benefits of strain-based SHM versus traditional inspections for corrosion-induced thickness loss in ship hulls.
A Bayesian hierarchical model integrates coherence penalization and level-specific focus into forecasting estimation, yielding improved predictive accuracy on simulated and Australian tourism data.
Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
Latent diffusion model parameterization allows MCMC and SMC to outperform latent-space ESMDA in data mismatch and uncertainty reduction for 3D subsurface DA, while model-space ESMDA produces unrealistic posteriors.
Bayesian joint model infers infectious virus shedding trajectories and derived infectiousness metrics from PCR and other proxies in SARS-CoV-2 using data from five cohorts of roughly 2000 infections.
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
Joint location-scale minimization for geometric medians on product manifolds degenerates to marginal medians, and three new scale-selection methods restore identifiability with asymptotic guarantees.
A new Bayesian multiscale framework with cut inference jointly models heterogeneous viral load trajectories and household transmission, recovering parameters without bias on simulated data when viral sampling is frequent.
Two hybrid Bayesian surrogate training approaches integrate simulation and real-world data via a weighting strategy independent of surrogate family, shown in synthetic and real case studies to improve accuracy and diagnose simulation issues.
Bayesian state-space modeling reveals that local spread and within-herd transmission each account for about half of S. Dublin force of infection on Öland, with average Rt near 1.
A reweighting method creates model-agnostic likelihoods from histogram analyses, applied to Belle II B+ to K+ nu nubar data for WET constraints and light new physics searches.
A review reframing density estimation as 'density evolution' across scales, linking kernel smoothing to heat flow, mixtures to compression, and topology to level sets, while stating three structural results on modes, Gaussian semigroups, and log-concavity.
A Bayesian mixed Hawkes process with Weibull baseline intensity and random effects is developed to model seizure clustering and heterogeneity in focal epilepsy from the Human Epilepsy Project data.
For 5-item subsets of the MDS-UPDRS, coordinate descent item selection cuts expected standard deviation of severity estimates by 26% and adaptive selection by 34% versus random choice, outperforming Fisher-information ranking by 12 percentage points.
BDARMA models applied to platform booking data forecast tourist origin market shares with 27% lower error than naive methods for EMEA regions while respecting the unit-sum constraint.
ATune combines Gaussian theoretical analysis with burn-in simulation data to select system-specific splitting integrators and hyperparameter credible intervals for improved HMC stability and performance.
SBI matches MCMC posterior accuracy on a SECIR model but runs 15-120 times faster on GPU for 31-day and 201-day inference windows.
A within-participants study with design students found that sketch inputs to an AI ideation tool increased fluency but students still preferred text prompts, pointing to design choices that could better preserve reflective practice.
Bulk flow measurements from Hawai`i Supernova Flows SNe Ia yield speeds of 100-400 km/s consistent with ΛCDM expectations at z ≲ 0.1.
citing papers explorer
-
Scale-Calibrated Median-of-Means for Robust Distributed Principal Component Analysis
Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
-
Hierarchical Bayes meets hierarchical forecasting: A flexible framework for level-focused forecasts
A Bayesian hierarchical model integrates coherence penalization and level-specific focus into forecasting estimation, yielding improved predictive accuracy on simulated and Australian tourism data.
-
To select or not to select: predictively consistent priors instead of model selection
Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
-
Inferring infectiousness: a joint model of the within-host viral kinetics of SARS-CoV-2
Bayesian joint model infers infectious virus shedding trajectories and derived infectiousness metrics from PCR and other proxies in SARS-CoV-2 using data from five cohorts of roughly 2000 infections.
-
Reliable model selection in the presence of parameter non-identifiability
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
-
A Mixed Self-Exciting Process to Model Epileptic Seizures
A Bayesian mixed Hawkes process with Weibull baseline intensity and random effects is developed to model seizure clustering and heterogeneity in focal epilepsy from the Human Epilepsy Project data.
-
Optimized questionnaire item selection for tracking the progression of motor symptoms in Parkinson's disease
For 5-item subsets of the MDS-UPDRS, coordinate descent item selection cuts expected standard deviation of severity estimates by 26% and adaptive selection by 34% versus random choice, outperforming Fisher-information ranking by 12 percentage points.