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A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning Ensemble Distributionally Robust Bayesian Optimisation The Proxy Presumption: From Semantic Embeddings to Valid Social Measures Modulated learning for private and distributed regression with just a single sample per client device Query-efficient model evaluation using cached responses Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks Optimal Experiments for Partial Causal Effect Identification Order-Agnostic Autoregressive Modelling with Missing Data Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes Tuning Derivatives for Causal Fairness in Machine Learning Spherical Flows for Sampling Categorical Data Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors GRALIS: A Unified Canonical Framework for Linear Attribution Methods via Riesz Representation Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics Perturbation is All You Need for Extrapolating Language Models Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization Realizable Bayes-Consistency for General Metric Losses Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution Segmenting Human-LLM Co-authored Text via Change Point Detection Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation Understanding Self-Supervised Learning via Latent Distribution Matching The Geometric Mechanics of Contrastive Representation Learning: Alignment Potentials, Entropic Dispersion, and Cross-modal Divergence Imbalanced Classification under Capacity Constraints On the Spectral Structure and Objective Equivalence of Orthogonal Multilabel Fisher Discriminants Partially Observed Structural Causal Models First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint Robust and Fast Training via Per-Sample Clipping
Tractable Approximation of Labeled Multi-Object Posterior...
[Submitted on 23 Sep 2025 (v1), last revised 24 Jul 2026 (this v · 2025-09-23 · via stat updates on arXiv.org

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Abstract:Multi-object estimation in state-space models (SSMs) wherein the system state is represented as a finite set has attracted significant interest in recent years. In Bayesian inference, the posterior density captures all information on the system trajectory since it considers the history of the states. In most multi-object SSM applications, closed-form multi-object posteriors are not available for non-standard multi-object models. Thus, functional approximation is necessary because these posteriors are very high-dimensional. This work provides a tractable multi-scan Generalized Labeled Multi-Bernoulli (GLMB) approximation that matches the trajectory cardinality distribution of the labeled multi-object posterior density. The proposed approximation is also proven to minimize the Kullback-Leibler divergence over a special class of multi-scan GLMB model. Additionally, we develop a tractable algorithm for computing the approximate multi-object posteriors over finite windows. Numerical multi-object tracking experiments, using a simulated social force model with uninformative observations, and a real-world social force pedestrian dataset are presented to validate the proposed approximation method.

Submission history

From: Thi Hong Thai Nguyen [view email]
[v1] Tue, 23 Sep 2025 08:16:44 UTC (254 KB)
[v2] Fri, 24 Jul 2026 06:58:33 UTC (320 KB)