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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.

168,657 papers · 148 categories

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87174261348 · Jun 202019922001200920172026
← all fields·9 papers on “bayesian inference” in Statistical ML · 1 month
PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Cluttercs.LG

PiVoT improves real-time multi-object detection and tracking in clutter.

problem Challenges in multi-object detection and tracking from noisy point clouds.
method Variational inference for fast, clutter-resilient multi-object tracking.
result Substantial performance improvement over existing Bayesian trackers.
multi-object trackingBayesian inferenceclutter resiliencereal-timepoint clouds
2026-07-15abs ↗pdf ↗
Dynamic Online Processor-Native Inference for State Estimationstat.ML

This paper presents a fast Bayesian filtering technique for state estimation.

problem Bottleneck in Bayesian inference for state estimation from noisy sensor data.
method Processor-native uncertainty tracking for uncertainty propagation and inference.
result Deterministic approximate filtering with up to 805x speedup and competitive accuracy.
Bayesian inferencestate estimationsensor dataMonte Carloparticle filters
2026-07-13abs ↗pdf ↗
Geometric Causal Modelsstat.ML

Develops geometric causal models for causal inference from dependent data.

problem Causal inference from structured, dependent data (e.g., spatial, network, molecular).
method Geometric causal models (GCMs) exploiting symmetries of data generating process, combining group theory, ergodic theory, and Bayesian inference.
result Establishes identification and estimation of causal effects from dependent data.
causal inferencedependent datasymmetrygroup theoryergodic theory
2026-07-06abs ↗pdf ↗
Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Datastat.ML

Integrates neural encoders into GLMMs for multimodal data analysis.

problem Scalable Bayesian inference for GLMMs assumes low-dimensional tabular predictors and does not handle high-dimensional modalities.
method Jointly learns modality-specific neural encoders with GLMM objective, performs variance-corrected stochastic-gradient MCMC.
result Preserves interpretable fixed and random effects while scaling to large longitudinal datasets.
Bayesian inferenceGeneralized Linear Mixed ModelsNeural EncodersMultimodal DataUncertainty Quantification
2026-07-06abs ↗pdf ↗
Sequential sparse Gaussian process quantile regressioncs.LG

Sparse Gaussian process quantile regression tackles computational challenges in Bayesian quantile regression.

problem Nonconjugacy and computational cost in Gaussian process quantile regression.
method Sparse Gaussian process framework with Laplace approximation, adaptive inducing-input placement, and sequential data acquisition.
result Accuracy of Laplace approximation and effectiveness of adaptive mechanisms in reducing predictive uncertainty.
quantile regressionGaussian processBayesian inferenceadaptive samplingsequential algorithm
2026-06-30abs ↗pdf ↗