{"total":22,"items":[{"citing_arxiv_id":"2606.27286","ref_index":37,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Simulation-based inference for rapid Bayesian parameter estimation in epidemiological models: a comparison with MCMC","primary_cat":"cs.AI","submitted_at":"2026-06-25T17:03:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.26626","ref_index":68,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Reviving Reflection-in-Action: Instilling Designerly Thinking in AI-Supported Ideation through Multimodal Prompting","primary_cat":"cs.HC","submitted_at":"2026-06-25T05:41:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.23148","ref_index":52,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Bayesian modelling of herd-level infection dynamics in cattle: Local spread as the primary driver of Salmonella Dublin persistence on \\\"Oland","primary_cat":"q-bio.PE","submitted_at":"2026-06-22T10:48:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.23009","ref_index":188,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Hierarchical Bayes meets hierarchical forecasting: A flexible framework for level-focused forecasts","primary_cat":"stat.ME","submitted_at":"2026-06-22T08:22:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"UNKNOWN","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A Bayesian hierarchical model integrates coherence penalization and level-specific focus into forecasting estimation, yielding improved predictive accuracy on simulated and Australian tourism data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.22850","ref_index":189,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"To select or not to select: predictively consistent priors instead of model selection","primary_cat":"stat.ME","submitted_at":"2026-06-22T04:52:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.22215","ref_index":148,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Accelerating Discovery: Model-Agnostic Likelihoods for the Reinterpretation of Particle Physics Results and their Application to the Belle II $B^{+}\\to K^{+}\\nu\\bar{\\nu}$ Measurement","primary_cat":"hep-ex","submitted_at":"2026-06-20T20:31:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.19148","ref_index":219,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Fast Computation of Free-Support Wasserstein Medians","primary_cat":"stat.CO","submitted_at":"2026-06-17T14:50:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.17142","ref_index":96,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Hawai`i Supernova Flows: Bulk Flow Measurements using SNe Ia in the Optical and NIR","primary_cat":"astro-ph.CO","submitted_at":"2026-06-15T18:00:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.11140","ref_index":57,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Data assimilation for subsurface flow using latent diffusion model parameterization: performance of ensemble-Kalman and Monte Carlo techniques","primary_cat":"physics.geo-ph","submitted_at":"2026-06-09T17:29:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09594","ref_index":61,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Constraint residuals, graph posteriors, and determinant-corrected full-space targets in Bayesian inverse problems","primary_cat":"math.ST","submitted_at":"2026-06-08T15:04:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00233","ref_index":177,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Density Evolution: A Multiscale View of Density Estimation","primary_cat":"math.ST","submitted_at":"2026-05-29T18:08:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22038","ref_index":103,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Mixed Self-Exciting Process to Model Epileptic Seizures","primary_cat":"stat.ME","submitted_at":"2026-05-21T06:21:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20692","ref_index":43,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Inferring infectiousness: a joint model of the within-host viral kinetics of SARS-CoV-2","primary_cat":"stat.ME","submitted_at":"2026-05-20T04:39:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20681","ref_index":53,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Scale-Calibrated Median-of-Means for Robust Distributed Principal Component Analysis","primary_cat":"stat.ME","submitted_at":"2026-05-20T03:48:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"r αvµ + (2−α)v U b ! ≤exp(−c 2K).(52) The theorem gives a bound in the scaled product metric, which leads to the following corollary that translates into the mean and the principal subspace. Corollary 13(Factorwise deviation tradeoff).Under the conditions of Theorem 12, with probability at least1−exp(−c 2K), ∥eµn,α −µ 0∥ ≤ c1√α r αvµ + (2−α)v U b ,(53) and dGr(eUn,α,U 0)≤ c1√2−α r αvµ + (2−α)v U b .(54) This corollary is the finite-sample analogue of the covariance tradeoff. Movingαtoward two may reduce the scaled effect of subspace noise, but it also weakens the direct subspace guarantee because the factor (2−α) −1/2 grows. Movingαtoward zero has the analogous effect on the mean. 6.2 Bad nodes and factorwise influence"},{"citing_arxiv_id":"2605.19807","ref_index":167,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Reliable model selection in the presence of parameter non-identifiability","primary_cat":"stat.ME","submitted_at":"2026-05-19T13:06:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08001","ref_index":33,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Scale selection for geometric medians on product manifolds","primary_cat":"math.ST","submitted_at":"2026-05-08T16:57:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"for a small fixedε >0, which prevents finite-sample metric collapse. 5.6 Dimension-adjusted calibration When the factors have very different dimensions, one may standardize dispersion per tangent di- mension rather than total marginal dispersion. Let d∗ M = dimM, d ∗ N = dimN. 14 The dimension-adjusted calibrated weight is bαdim = 2 d∗ M /bs2 M d∗ M /bs2 M +d ∗ N /bs2 N .(33) All consistency and two-step CLT results above apply with the obvious modification of the map (sM , sN)7→α. 6 Approach III: a balanced estimating equation forα Scale calibration choosesαby marginal standardization. We now give a direct data-adaptive al- ternative: add a bounded estimating equation that balances the two factor contributions. This is"},{"citing_arxiv_id":"2604.20069","ref_index":42,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Bayesian inference for disease transmission models informed by viral dynamics","primary_cat":"stat.AP","submitted_at":"2026-04-22T00:30:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.11239","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Optimized questionnaire item selection for tracking the progression of motor symptoms in Parkinson's disease","primary_cat":"stat.ME","submitted_at":"2026-04-13T09:44:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.18358","ref_index":39,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Forecasting the Evolving Composition of Inbound Tourism Demand: A Bayesian Compositional Time Series Approach Using Platform Booking Data","primary_cat":"stat.AP","submitted_at":"2026-02-20T17:09:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2506.04082","ref_index":50,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Adaptive tuning of Hamiltonian Monte Carlo methods","primary_cat":"stat.CO","submitted_at":"2025-06-04T15:44:32+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":5.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.07427","ref_index":58,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Value of Information-based assessment of strain-based thickness loss monitoring in ship hull structures","primary_cat":"stat.AP","submitted_at":"2025-05-12T10:34:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Applies value-of-information decision analysis to quantify benefits of strain-based SHM versus traditional inspections for corrosion-induced thickness loss in ship hulls.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2412.11875","ref_index":41,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy","primary_cat":"stat.ML","submitted_at":"2024-12-16T15:27:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}