On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

Trend · papers per month

count % of corpus

7 results for “g-prior”

Sampling-based inference for large linear models, with application to linearised Laplacestat.ML

A scalable method for Bayesian inference in large linear models.

problem High computational cost in Bayesian linear models for large networks.

method Sample-based inference and g-prior for hyperparameter selection.

result Linearised neural network inference on large datasets (ResNet-18, ResNet-50, U-Net).

Bayesian inference large linear models sample-based methods g-prior neural network uncertainty

2022-10-10abs ↗pdf ↗

Consistent Bayesian causal discovery for structural equation models with equal error variancesmath.ST

Bayesian method recovers causal structure in SEMs with equal error variances.

problem Recovering causal structure in SEMs with equal error variances.

method Bayesian DAG selection method using g-priors and the key property of minimum expected squared errors.

result The method consistently recovers the true graph without additional distributional assumptions.

Bayesian causal discovery structural equation models equal error variances consistent recovery g-priors

2025-09-18abs ↗pdf ↗

Penalised regression with multiple sources of prior effectsstat.ME

The paper proposes a method to integrate prior information into penalized regression.

problem Improving predictive performance in high-dimensional tasks with prior information.

method Integrating multiple sources of prior information into penalized regression.

result The method improves predictive performance, as shown by simulations and applications.

penalized regression prior information epigenetic markers R package

2022-12-16abs ↗pdf ↗

Bayesian Adaptive Polynomial Chaos Expansionsstat.ME

Bayesian adaptive PCE method improves surrogate modeling and sensitivity analysis.

problem Lack of fully Bayesian PCE methods in statistics.

method Developed a novel fully Bayesian adaptive PCE method with R implementation.

result Bayesian adaptive PCE provides competitive performance for various UQ tasks.

Bayesian Polynomial chaos expansions Uncertainty quantification Machine learning Sensitivity analysis

2025-10-28abs ↗pdf ↗

Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resamplingcs.LG

Study assesses whether RL algorithm personalizes treatment sequences.

problem Evaluate if RL algorithm truly personalizes treatment sequences.

method Resampling-based methodology to investigate personalization.

result RL algorithm's personalization may be due to stochasticity.

reinforcement learning personalization resampling healthcare clinical trial

2023-04-11abs ↗pdf ↗

Anomaly Detection with Score Distribution Discriminationcs.LG

A new method optimizes anomaly scoring from score distribution to improve AD performance.

problem Vulnerability to anomaly contamination and lack of adaptability in existing AD methods.

method Optimizes anomaly scoring function from score distribution perspective, using Overlap loss.

result Overlap loss-based AD models significantly outperform state-of-the-art methods.

anomaly detection score distribution overlap loss adversarial learning data contamination

2023-06-26abs ↗pdf ↗

Predicting Stock Market Crash with Bayesian Generalised Pareto Regressionq-fin.ST

Bayesian GPR model predicts extreme stock market losses.

problem Forecasting rare but impactful extreme negative returns in equity markets.

method Developed a Bayesian Generalised Pareto Regression model linking scale parameter to market volatility.

result The Cauchy prior provides the best balance between predictive accuracy and model simplicity.

Bayesian GPR Extreme Value Theory Tail Risk Stock Market Crash Volatility

2025-06-21abs ↗pdf ↗