The Ehresmann connection induced by the Fisher metric on the hierarchical parameter fiber bundle is flat for any smooth posterior, identifying the mixing obstruction as the prior fraction (pooling factor) rather than curvature.
Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
23 Pith papers cite this work, alongside 143 external citations. Polarity classification is still indexing.
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Introduces symmetry-aware convex shrinkage for high-dimensional covariance estimation by selecting a symmetry group via held-out negative log-likelihood and proving regret bounds plus dominance over Ledoit-Wolf under a match condition.
The PMNLV model extends single-neuron overdispersion to populations via matrix-normal gain priors, showing shared co-variability highest in V1 and declining along the mouse visual hierarchy.
Exposure-integrated Gaussian processes allow prediction of both latent stellar signals and instrument-specific binned versions, supporting combination of overlapping EPRV datasets with varying exposure times.
Derives augmented IPW estimators for occupation probabilities in coarsened multistate processes under coarsening at random, allowing time-varying confounders.
A landmarking approach using latent class mixed models for dynamic prediction of time-to-event data that accounts for latent heterogeneity in longitudinal biomarker trajectories.
WRaPs extends optimally weighted random effect estimators to joint models, providing closed-form solutions for basic cases and MCMC computation for complex ones to predict extreme random effects while accounting for survival data.
A deep learning method amortizes probabilistic XCO2 retrieval from OCO-2 spectra via Laplace approximations and normalizing flows, trained on simulations with model errors to achieve faster inference and better-calibrated uncertainties than operational solvers.
DAG analysis shows univariate screening before multivariate modeling reduces inclusion of non-causal features in molecular signatures of lifestyle exposures.
A finite-element variational inference method delivers full-covariance Bayesian field reconstruction at dimensions exceeding 400,000 for 3D porous media flow using sparse precision parameterization from SPDE priors.
Proposes an inferential framework to test differences in categorical Gini correlations for predictor importance in classification, establishing asymptotic normality and consistency while accommodating unequal dimensions and dependence.
Nonparametric Bayesian methods with Gaussian and Besov-Laplace priors achieve minimax-optimal posterior contraction rates for covariate-driven point process intensity estimation under increasing domain asymptotics with ergodic covariates.
A Bayesian model for multi-feature contact matrices that uses tensor structures and contingency table theory to satisfy structural constraints and impute missing contact features, validated on simulations and US/German survey data.
SVBR is a new hierarchical Bayesian method that treats buffer radii as unknown spatially varying parameters, improves parameter recovery in simulations, and reveals spatial heterogeneity in healthcare access effects on antenatal care in Madagascar.
A hybrid INLA-RF framework integrates Bayesian spatio-temporal modeling with random forests through two iterative algorithms to improve predictions and uncertainty quantification for environmental data.
Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.
Mathematical analysis shows sparse linear regression mitigates output dimension collapse in brain-to-image reconstruction at small data scales by exploiting sparsity in the brain-to-feature mapping.
Review and simulation comparison of more than 40 threshold selection procedures for univariate extreme value analysis, with application to daily rainfall data.
Develops SICS and RCRS screening methods for consistent selection of sparse active predictors and change points in high-dimensional structural break predictive regressions that may involve stationary or cointegrated series.
A multimodal GNN ablation for Nordic precipitation nowcasting shows sparse point observations improve station and onset scores while NWP and CRPS losses improve radar-grid performance, indicating local and field skills are distinct targets.
Proves reverse Poincaré inequality on global attractor of 2D reaction-diffusion system to obtain near-parametric statistical recovery of initial conditions from discrete observations.
Football fever in spectators follows a V-shaped time course captured as a latent process from heart rate and stress data via time-dependent structural equation modeling.
Implicit neural representations enable stable, resolution-independent reconstruction of continuous environmental fields from sparse and irregular ecological data.
citing papers explorer
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A Flat Connection: The Pooling Factor and the Geometry of Centring in Hierarchical MCMC
The Ehresmann connection induced by the Fisher metric on the hierarchical parameter fiber bundle is flat for any smooth posterior, identifying the mixing obstruction as the prior fraction (pooling factor) rather than curvature.
-
Symmetry-Aware Convex Shrinkage for High-Dimensional Covariance Estimation
Introduces symmetry-aware convex shrinkage for high-dimensional covariance estimation by selecting a symmetry group via held-out negative log-likelihood and proving regret bounds plus dominance over Ledoit-Wolf under a match condition.
-
Partitioning Neural Co-Variability
The PMNLV model extends single-neuron overdispersion to populations via matrix-normal gain priors, showing shared co-variability highest in V1 and declining along the mouse visual hierarchy.
-
Exposure-averaged Gaussian Processes for Combining Overlapping Datasets
Exposure-integrated Gaussian processes allow prediction of both latent stellar signals and instrument-specific binned versions, supporting combination of overlapping EPRV datasets with varying exposure times.
-
Robust estimation of occupation probabilities for coarsened multistate processes
Derives augmented IPW estimators for occupation probabilities in coarsened multistate processes under coarsening at random, allowing time-varying confounders.
-
Landmarking with Latent Class Mixed Models for Dynamic Prediction of Time-to-event Data with Heterogeneous Biomarker Trajectories
A landmarking approach using latent class mixed models for dynamic prediction of time-to-event data that accounts for latent heterogeneity in longitudinal biomarker trajectories.
-
Improved prediction of extreme random effects in joint models: WRaPs
WRaPs extends optimally weighted random effect estimators to joint models, providing closed-form solutions for basic cases and MCMC computation for complex ones to predict extreme random effects while accounting for survival data.
-
Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows
A deep learning method amortizes probabilistic XCO2 retrieval from OCO-2 spectra via Laplace approximations and normalizing flows, trained on simulations with model errors to achieve faster inference and better-calibrated uncertainties than operational solvers.
-
Considering causality in the construction of molecular signatures of lifestyle exposures
DAG analysis shows univariate screening before multivariate modeling reduces inclusion of non-causal features in molecular signatures of lifestyle exposures.
-
Scalable High-Dimensional Bayesian Field Reconstruction with Finite Elements: Application to 3D Porous Media Flow
A finite-element variational inference method delivers full-covariance Bayesian field reconstruction at dimensions exceeding 400,000 for 3D porous media flow using sparse precision parameterization from SPDE priors.
-
Comparing Two Categorical Gini Correlations with Applications to Classification Problems
Proposes an inferential framework to test differences in categorical Gini correlations for predictor importance in classification, establishing asymptotic normality and consistency while accommodating unequal dimensions and dependence.
-
Increasing domain asymptotics for covariate-based nonparametric Bayesian intensity estimation with Gaussian and Besov-Laplace priors
Nonparametric Bayesian methods with Gaussian and Besov-Laplace priors achieve minimax-optimal posterior contraction rates for covariate-driven point process intensity estimation under increasing domain asymptotics with ergodic covariates.
-
Bayesian Modeling and Prediction of Generalized Contact Matrices
A Bayesian model for multi-feature contact matrices that uses tensor structures and contingency table theory to satisfy structural constraints and impute missing contact features, validated on simulations and US/German survey data.
-
Modeling Spatial Heterogeneity in Exposure Buffers and Risk: A Hierarchical Bayesian Approach
SVBR is a new hierarchical Bayesian method that treats buffer radii as unknown spatially varying parameters, improves parameter recovery in simulations, and reveals spatial heterogeneity in healthcare access effects on antenatal care in Madagascar.
-
INLA-RF: A Hybrid Modeling Strategy for Spatio-Temporal Environmental Data
A hybrid INLA-RF framework integrates Bayesian spatio-temporal modeling with random forests through two iterative algorithms to improve predictions and uncertainty quantification for environmental data.
-
Restricted Multivariate Spatial Modeling
Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.
-
Overcoming Output Dimension Collapse: When Sparsity Enables Zero-shot Brain-to-Image Reconstruction at Small Data Scales
Mathematical analysis shows sparse linear regression mitigates output dimension collapse in brain-to-image reconstruction at small data scales by exploiting sparsity in the brain-to-feature mapping.
-
Choosing the threshold in extreme value analysis
Review and simulation comparison of more than 40 threshold selection procedures for univariate extreme value analysis, with application to daily rainfall data.
-
Feature Screening for High-Dimensional Structural Break Predictive Regression
Develops SICS and RCRS screening methods for consistent selection of sparse active predictors and change points in high-dimensional structural break predictive regressions that may involve stationary or cointegrated series.
-
Pointwise is Pointless? A Multimodal Ablation Study for Precipitation Nowcasting with Graph Neural Networks
A multimodal GNN ablation for Nordic precipitation nowcasting shows sparse point observations improve station and onset scores while NWP and CRPS losses improve radar-grid performance, indicating local and field skills are distinct targets.
-
On statistical inference for non-linear dynamical systems evolving in their global attractor
Proves reverse Poincaré inequality on global attractor of 2D reaction-diffusion system to obtain near-parametric statistical recovery of initial conditions from discrete observations.
-
Time-dependent structural equation modeling of fans' football fever using activity tracking data during the 2025 DFB Cup final
Football fever in spectators follows a V-shaped time course captured as a latent process from heart rate and stress data via time-dependent structural equation modeling.
-
Implicit neural representations as a coordinate-based framework for continuous environmental field reconstruction from sparse ecological observations
Implicit neural representations enable stable, resolution-independent reconstruction of continuous environmental fields from sparse and irregular ecological data.