Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
Rank-normalization, folding, and localization: An improved R for assessing convergence of MCMC
11 Pith papers cite this work. Polarity classification is still indexing.
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Applies value-of-information decision analysis to quantify benefits of strain-based SHM versus traditional inspections for corrosion-induced thickness loss in ship hulls.
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.
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.
citing papers explorer
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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.
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Value of Information-based assessment of strain-based thickness loss monitoring in ship hull structures
Applies value-of-information decision analysis to quantify benefits of strain-based SHM versus traditional inspections for corrosion-induced thickness loss in ship hulls.
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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.
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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.
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Scale selection for geometric medians on product manifolds
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.
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Bayesian inference for disease transmission models informed by viral dynamics
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.
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Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy
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.
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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.
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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.
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Forecasting the Evolving Composition of Inbound Tourism Demand: A Bayesian Compositional Time Series Approach Using Platform Booking Data
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.
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Adaptive tuning of Hamiltonian Monte Carlo methods
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.