Dirichlet Process Mixture Models

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9 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Dirichlet Process Mixtures within Semiparametric Bayesian Networks: A Novel Conditional Density Estimation Approach
Oct 26, 2026Victor Alejandre, Pedro Larrañaga, Concha BielzaBayesian Networks Conditional Distribution Estimation
Computational Intelligence Group Departamento de Inteligencia Artificial Universidad Politécnica de Madrid Campus de Montegancedo, Boadilla del Monte 28660 Madrid, Spain
Dirichlet Process Mixtures of Trees with Gaussian Process Splits: A Bayesian Nonparametric Framework with Posterior Contraction Rate
Aug 22, 2026Subhasish Basak, Anik Roy, Sourabh BhattacharyaDirichlet Process Mixture Models Posterior Contraction
Indian Statistical Institute
DP-BOA: Dirichlet-Process Birth-or-Assign for On-the-Fly Category Discovery
Jul 15, 2026Peiyan Gu, Zixin Teng, Xuming HeDirichlet Process Mixture Models Bayesian Mixture Models
ShanghaiTech University, Shanghai, China · Shanghai Engineering Research Center of Intelligent Vision and Imaging
DP-Splat: Bayesian Nonparametric Complexity Control for Gaussian Splatting
Jul 12, 2026Aqi DongBayesian Mixture Models Dirichlet Process Mixture Models
Embry-Riddle Aeronautical University
BPDA-GMM: Bayesian Probabilistic Data Association via Gaussian Mixture Models for Semantic SLAM
Jun 3, 2026Thanh Nguyen Canh, Haolan Zhang, Xiem HoangVan +2Simultaneous Localization and Mapping Dirichlet Process Mixture Models
School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa, 923-1211, Japan · University of Engineering and Technology, Vietnam National University, Hanoi, 10000, Vietnam
Courtroom Analogy: New Perspective on Uncertainty-Aware Classification
May 25, 2026Taeseong Yoon, Heeyoung KimDirichlet Process Mixture Models Uncertainty Quantification
Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
Enhancing Multi-Robot Exploration Using Probabilistic Frontier Prioritization with Dirichlet Process Gaussian Mixtures
Apr 3, 2026John Lewis Devassy, Meysam Basiri, Mário A. T. Figueiredo +1Dirichlet Process Mixture Models Multi-Robot Exploration
Institute for Systems and Robotics / LARSyS and Instituto Superior Técnico, Universidade de Lisboa, Lisbon 1049-001, Portugal · Instituto de Telecomunicações and Instituto Superior Técnico, Universidade de Lisboa, Lisbon 1049-001, Portugal
Deconfounded Lifelong Learning for Autonomous Driving via Dynamic Knowledge Spaces
Mar 15, 2026Jiayuan Du, Yuebing Song, Yiming Zhao +6Dirichlet Process Mixture Models End-to-End Autonomous Driving
Tongji University, Shanghai, China
Prognostics of Multisensor Systems with Unknown and Unlabeled Failure Modes via Bayesian Nonparametric Process Mixtures
Feb 22, 2026Kani Fu, Sanduni S Disanayaka Mudiyanselage, Chunli Dai +1Dirichlet Process Mixture Models Predictive Maintenance
Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA · School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, USA

Oct 26, 2026J/K move · Enter open · S save

Dirichlet Process Mixtures within Semiparametric Bayesian Networks: A Novel Conditional Density Estimation Approach

Victor Alejandre, Pedro Larrañaga, Concha Bielza

Computational Intelligence Group Departamento de Inteligencia Artificial Universidad Politécnica de Madrid Campus de Montegancedo, Boadilla del Monte 28660 Madrid, Spain

Bayesian Networks Conditional Distribution Estimation Dirichlet Process Mixture Models Bayesian Network Structure Learning

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Semiparametric Bayesian networks (SPBNs) are flexible probabilistic graphical models for representing joint distributions of continuous variables through lower-dimensional conditional probability distributions (CPDs). Existing SPBN models use kernel density estimators (KDEs) for non-parametric CPD modeling, but their one-component-per-observation mixture representation can lead to increased inference costs and density estimation errors, which can be limiting. In this work, we define a new CPD modeling approach for non-parametric components based on Bayesian non-parametric Dirichlet Process Mixtures (DPMs) within SPBNs. DPMs provide a data-adaptive clustering of observations, yielding a more compact representation while retaining the flexibility of non-parametric density estimation, unlike KDE. This results in lower inference costs while improving density estimation performance. We implement the proposed DPM-based SPBN (DPM-SPBN) by extending the open-source PyBNesian package and evaluate it on synthetic and real-world data. For a given network structure, DPM-based CPDs significantly outperform state-of-the-art Bayesian network models for continuous variables in density estimation, achieving lower absolute log-likelihood errors on synthetic data. We further investigate the impact of the proposed CPD modeling on structure learning methods relying on CPD-based scores. DPM-based SPBNs achieve lower structure learning and density estimation errors than KDE-based SPBNs. Experiments on real-world data corroborate these findings, identifying DPM-based CPD modeling as the most effective alternative among the evaluated non-parametric approaches for continuous variables.