Bayesian Mixture Models

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Overview

Bayesian mixture models represent data as arising from several latent groups, using probability distributions over both component parameters and the assignments of observations to components. Finite mixtures and nonparametric approaches such as Dirichlet-process and Pitman–Yor mixtures support clustering, density estimation, online category discovery, and mixture-of-experts models. Researchers use Markov chain Monte Carlo and variational inference to fit these models, including in settings such as distributed data, neural networks, and synthetic-data generation. A central challenge is balancing flexible discovery of the number and shape of components with reliable, scalable inference, since multimodality, label switching, and mode collapse can make estimates difficult to compute and interpret.

Latest papers

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

  1. Test-Time Adaptation of Reasoning Strategies with Bayesian Nonparametric Memory

    Oct 5, 2026Keshav Ramji, Tahira Naseem, Ramón Fernandez AstudilloMemory-Augmented Language ModelsBayesian Mixture Models

    IBM Research AI

  2. Sharp Minimax Regret for Infinite-Memory Logistic Prediction

    Aug 27, 2026Vaneet AggarwalLogistic RegressionBayesian Mixture Models

    Purdue University

  3. PYPM-GGD: Pitman-Yor Process Mixture with Generalized Gaussian Density using ADAM

    Jul 27, 2026Kart-Leong LimBayesian Mixture ModelsClustering

    Institute of Microelectronics, A*Star Singapore

  4. Distributional Determinantal Point Process for Repulsive Clustering of Distributions

    Jul 23, 2026Khai Nguyen, Yang Ni, Elizabeth Juarez-Colunga +1Bayesian Mixture ModelsClustering

    Department of Statistics and Data Sciences, University of Texas at Austin · Department of Biostatistics and Informatics, University of Colorado Anschutz · Department of Mathematics, University of Texas at Austin

  5. DP-BOA: Dirichlet-Process Birth-or-Assign for On-the-Fly Category Discovery

    Jul 15, 2026Peiyan Gu, Zixin Teng, Xuming HeDirichlet Process Mixture ModelsBayesian Mixture Models

    ShanghaiTech University, Shanghai, China · Shanghai Engineering Research Center of Intelligent Vision and Imaging

  6. DP-Splat: Bayesian Nonparametric Complexity Control for Gaussian Splatting

    Jul 12, 2026Aqi DongBayesian Mixture ModelsDirichlet Process Mixture Models

    Embry-Riddle Aeronautical University

  7. BACH: A Bayesian Admixture of Contrastive Heads for Multi-Interest Two-Tower Retrieval

    Jul 9, 2026Quoc Phong Nguyen, Paul Albert, Long Vuong +2Bayesian Mixture ModelsMulti-Vector Retrieval

    Amazon

  8. TDGT: A Tabular Data Generation Toolkit supporting adaptive GPU-accelerated Bayesian mixture models, diffusion-based models, and latent-space generative modeling

    Jun 30, 2026Vasileios C. Pezoulas, Nikolaos S. Tachos, Eleni Georga +3Bayesian Mixture ModelsBayesian Inference

    SYNTHAINA AI, Ioannina, Greece

  9. Variational Consensus Monte Carlo for Bayesian Mixture

    Jun 17, 2026Julie Fendler, Francesca L. Crowe, Tom Marshall +2Markov Chain Monte CarloFederated Learning Aggregation

    MRC Biostatistics Unit, University of Cambridge · Institute of Applied Health Research, University of Birmingham

  10. Folded Transport MCMC: Eliminating Label Switching by Sampling on a Fundamental Domain

    Jun 3, 2026Jun HuMarkov Chain Monte CarloBayesian Mixture Models

    Wuhan University of Technology

  11. Heterogeneous Variational Inference for Markov Degradation Hazard Models: Discretized Mixture with Interpretable Clusters

    Apr 27, 2026Takato YasunoLatent Variable ModelsMarkov Models

    Digital Business Department, Yachiyo Engineering Co., Ltd.

  12. On Bayesian Softmax-Gated Mixture-of-Experts Models

    Apr 22, 2026Nicola Bariletto, Huy Nguyen, Nhat Ho +1Bayesian Mixture ModelsBayesian Inference

    The University of Texas at Austin

  13. Dirichlet Scale Mixture Priors for Bayesian Neural Networks

    Feb 23, 2026August Arnstad, Leiv Rønneberg, Geir StorvikBayesian Neural NetworksSparse Neural Networks

    Department of Statistics & Data Science, University of Oslo.

  14. 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 ModelsPredictive Maintenance

    Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA · School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, USA

  15. Mode Collapse of Mean-Field Variational Inference

    Oct 20, 2025Shunan Sheng, Bohan Wu, Alberto González-SanzVariational InferenceMode Collapse

    Columbia University, Department of Statistics · Department of Statistics, Columbia University

  16. Inverse Problem for Partial Differential Equations with Jump Discontinuities in Coefficients by Two-stage Physics-Informed Deep Learning and Statistical Mixture Models

    Oct 16, 2025Zhikun Zhang, Guanyu Pan, Xiangjun Wang +2Bayesian Mixture ModelsPhysics-Informed ML

    School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710072, China · School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China · SandGold AI Research, Guangzhou 510642, China +3

  17. Bayesian Nonparametric Thompson Sampling for Multi-Armed Bandits

    Aug 8, 2018Iñigo Urteaga, Chris H. WigginsMulti-Armed BanditsThompson Sampling

    Department of Applied Physics and Applied Mathematics Data Science Institute Columbia University New York City, NY 10027