{"total":14,"items":[{"citing_arxiv_id":"2606.26505","ref_index":47,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Same Scrutiny, More Time: Eye Tracking Insights into Reviewing LLM-Labelled Code","primary_cat":"cs.SE","submitted_at":"2026-06-25T01:23:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Eye-tracking experiment finds that labeling code as LLM-generated increases fixation time without changing review thoroughness, with reviewers adapting criteria or using the prompt.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.22850","ref_index":184,"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":"2606.08692","ref_index":10,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Logistic Credibility with Temporal Decay: Extending B\\\"uhlmann--Straub for Commercial Lines","primary_cat":"stat.AP","submitted_at":"2026-06-07T15:45:09+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A logistic credibility model with data-driven temporal decay restores calibration slope to 1.00 and reduces exposure-weighted error by 38% versus standard Bühlmann-Straub on US commercial auto held-out data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00231","ref_index":28,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"On Asymptotic Outlier Rejection in Bayesian Mixed Poisson Regression Models Under Extreme Target and Covariate Values","primary_cat":"stat.ME","submitted_at":"2026-05-29T18:06:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Mixed Poisson regression models with Gaussian latent variables are asymptotically robust to infinite target values but not to infinite covariate values, as shown for Poisson-Gamma, Poisson-log-t, and Poisson-RSB targets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.24601","ref_index":1,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Bayesian Conformal-Projective Prediction","primary_cat":"stat.ME","submitted_at":"2026-05-23T14:33:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces CPP framework using distributional conformity via leave-one-out predictive distributions, with proofs of dominance over plug-in predictors under contamination and closed-form solutions for linear models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22038","ref_index":128,"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":"2605.21782","ref_index":116,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Scalable Parametric Item Calibration Engine (SPICE) for Explanatory IRT with Sparse Data","primary_cat":"stat.ME","submitted_at":"2026-05-20T22:22:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SPICE is a scalable Bayesian MCMC engine for explanatory IRT calibration on sparsely linked persons and items in large assessment banks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06742","ref_index":98,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Bayesian Modeling and Prediction of Generalized Contact Matrices","primary_cat":"stat.ME","submitted_at":"2026-05-07T14:30:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.04564","ref_index":117,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Practical validation of synthetic pre-crash scenarios","primary_cat":"cs.RO","submitted_at":"2026-05-06T07:08:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A binning-based Bayesian ROPE equivalence testing method is introduced to quantitatively assess practical equivalence between synthetic and real pre-crash scenario datasets for driving automation safety impact evaluation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.16193","ref_index":33,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Correcting socioeconomic bias in mobile phone mobility estimates using multilevel regression and poststratification","primary_cat":"physics.soc-ph","submitted_at":"2026-04-17T16:01:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Multilevel regression and poststratification corrects socioeconomic sampling bias in CDR mobility estimates, lowering average radius of gyration by 17 percent.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.11929","ref_index":20,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Fast and principled equation discovery from chaos to climate","primary_cat":"cs.LG","submitted_at":"2026-04-13T18:17:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Bayesian-ARGOS is a hybrid frequentist-Bayesian method that discovers equations from limited noisy observations more efficiently than SINDy or bootstrap-ARGOS while adding uncertainty quantification.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"high-order nonlinear terms (Thomas, Lorenz, and Aizawa systems). Critically, these improvements are accompanied by an approximate 100-fold computational speedup relative to ARGOS, establishing practical viability for large-scale applications. Beyond performance metrics, the probabilistic formulation enables the deployment of standard statistical diagnostics, including PSIS-LOO [20] for influential observa- tions, VIF [21] for multicollinearity, and residual analysis for model misspecification, providing diagnostic signals for failure modes that are otherwise opaque. Importantly, the method's modular design facilitates integration with complementary identifi- cation pipelines for addressing higher-dimensional challenges. We demonstrate this"},{"citing_arxiv_id":"2604.06472","ref_index":35,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Data-Driven Constraints on Magnetar Population: No Evidence for a Distinct White Dwarf Channel","primary_cat":"astro-ph.HE","submitted_at":"2026-04-07T21:16:15+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.04346","ref_index":32,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Environment-Aware Indoor LoRaWAN Path Loss: Parametric Regression Comparisons, Shadow Fading, and Calibrated Fade Margins","primary_cat":"cs.NI","submitted_at":"2025-10-05T20:14:48+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Environment-conditioned parametric regression on 12-month indoor LoRaWAN data reduces cross-validated RMSE from 8.23 dB to 7.38 dB and lowers the fade margin needed for 99% reliability from ~28 dB to 25.73 dB.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2412.11875","ref_index":38,"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}