Markov Random Fields

Ask about this topic

Overview

Markov random fields represent joint distributions with an undirected graph, where connected variables interact through local potential functions; they are useful for modeling spatial and relational dependencies. Researchers develop methods for finding the most likely configuration (MAP inference), computing marginal probabilities, sampling, and learning graph structure or potentials. Approaches include message passing, graph cuts, optimization and variational methods, as well as neural and hybrid models for tasks such as image prediction, spatial segmentation, and categorical data. A central challenge is that exact inference is generally computationally difficult, so work balances accuracy and guarantees against the speed and scalability of approximate methods.

Latest papers

CardsList

16 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

  1. Covariate-dependent Joint Modeling of Multivariate Ordinal Preferences and Its Connections with Comparison Models

    Oct 6, 2026Yujie Chen, Antik Chakraborty, Anindya BhadraPreference ModelingPreference Learning

    Department of Statistics Purdue University

  2. Sample complexity bounds for categorical Markov random fields via Discrete Diffusions

    Oct 1, 2026Shivam Kumar, Nabarun DebMarkov Random FieldsDiffusion Sampling

    Booth School of Business, University of Chicago

  3. Prior-SG: Task and Prior Driven Region Segmentation for Scene Graphs in Arbitrarily-Structured Environments

    Aug 6, 2026Giorgio Tonetti, Laurent Kneip, Abel Gawel +1Open-Vocabulary 3D SegmentationMarkov Random Fields

    RAI Institute · ETH Zurich

  4. A Bayes-Markov Neuromorphic Model of Cortical Orientation Selectivity: A Computational Re-implementation and Quantitative Simulation Study

    Aug 1, 2026Abolfazl Moslemi, Milad Sarabadani, Fatemeh Sefidian +1Markov Random FieldsBayesian Inference

    Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran · School of Mathematics, Statistics, and Computer Science, Faculty of Science, University of Tehran, Tehran, Iran · Department of Psychology, Islamic Azad University, Tehran, Iran +1

  5. An End-to-End Hybrid Quantum--Classical Sampling Workflow for Discrete Markov Random Fields: A Reproducible Case Study

    Jul 10, 2026Arul Rhik MazumderMarkov Chain Monte CarloMarkov Random Fields

    School of Computer Science

  6. Zero-Shot Active Feature Acquisition via LLM-Elicitation

    Jun 17, 2026Binyamin Perets, Natalie Mendelson, Shiran Vainberg +3Zero-Shot LearningMarkov Random Fields

    Faculty of Medicine, Technion. · NVIDIA

  7. Approximating Gaussian Whittle-Matern Fields over Well-Centered Triangulations of Riemannian Manifolds

    Jun 11, 2026Srinivas NambirajanMarkov Random FieldsRiemannian Geometry

  8. IOAH3: Importance-Driven Adaptive Spatial Partitioning

    Jun 7, 2026Ehsaneddin JalilianMarkov Random Fields

    GeoSocial Artificial Intelligence, Interdisciplinary Transformation University Austria

  9. Predictive Coding with Bayesian Priors via Proximal Gradients

    Jun 6, 2026Francesco BulloPredictive CodingMarkov Random Fields

    Mechanical Engineering, 2325 Engineering Bldg II, UCSB, Santa Barbara, CA 93106-5070, USA

  10. JACoP: Joint Alignment for Compliant Multi-Agent Prediction

    May 12, 2026Qingze Liu, Alen Mrdovic, Danrui Li +3Pedestrian Trajectory PredictionMarkov Random Fields

    Rutgers University, New Brunswick · The College of New Jersey

  11. Binary Expansion Group Intersection Network

    Mar 25, 2026Sicheng Zhou, Kai ZhangProbabilistic Graphical ModelsMarkov Random Fields

    Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology, Cambridge, MA 02139. · Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599.

  12. State Algebra for Probabilistic Logic

    Mar 13, 2026Dmitry Lesnik, Tobias SchäferProbabilistic LogicMarkov Random Fields

    Stratyfy Inc., New York, NY, USA · Department of Mathematics, College of Staten Island, Staten Island, NY, USA & Physics Program, CUNY Graduate Center, NY, USA

  13. Learning Bayesian and Markov Networks with an Unreliable Oracle

    Mar 10, 2026Juha Harviainen, Pekka Parviainen, Vidya Sagar SharmaGraph Structure LearningBayesian Network Structure Learning

    University of Helsinki, Finland · University of Bergen, Norway · Indian Institute of Technology Madras, India

  14. Gaussian Belief Propagation Network for Depth Completion

    Jan 29, 2026Jie Tang, Pingping Xie, Jian Li +1Markov Random FieldsDepth Estimation

    National University of Defense Technology, Changsha, China · Hong Kong University of Science and Technology, Hong Kong, China

  15. ReMAP: Lossless Residual Neural Lifting for Scalable MAP Inference in Arbitrary-Order Markov Random Fields

    Nov 28, 2024Yaomin Wang, Chaolong Ying, Xiaodong Luo +1MAP EstimationMarkov Random Fields

    School of Data Science, The Chinese University of Hong Kong, Shenzhen · Shenzhen Research Institute of Big Data, China

  16. A polynomial time algebraic solution to exact marginal inference in Markov Random Field models

    Sep 25, 2017Ikhlef BecharEfficient InferenceMarkov Random Fields

    Aix Marseille Univ, Universit´e de Toulon, CNRS, ENSAM, LSIS La Garde 83957, France