MAP Estimation

MAP: Maximum a Posteriori

Latest papers

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

Principled MAP estimation for inverse problems: bridging the gap between convergence and performance
Sep 29, 2026Alexandre Lagier, Valentine Tosel, Anne Gagneux +2Bayesian Inverse Problems Bayesian Inference
ENS de Lyon, CNRS, Université Claude Bernard Lyon 1, Inria, LIP UMR 5668, 69342 Lyon Cedex 07, France · Univ. Bordeaux, Inria, Bordeaux INP, IMB, UMR 5251, F-33400 Talence, France · Inria, ENS de Lyon, CNRS, Université Claude Bernard Lyon 1, LIP UMR 5668, 69342 Lyon Cedex 07, France
PG-MAP: Joint MAP Optimization for Inference-Time Alignment of Diffusion and Flow-Matching Models
Jun 22, 2026Ruolan Sun, Pawel PolakDiffusion Model Alignment Inference-Time Language Model Alignment
Stony Brook University
Predictive Coding with Bayesian Priors via Proximal Gradients
Jun 6, 2026Francesco BulloPredictive Coding Markov Random Fields
Mechanical Engineering, 2325 Engineering Bldg II, UCSB, Santa Barbara, CA 93106-5070, USA
Boosting Image Quality Assessment Performance: Unsupervised Score Fusion by Deep Maximum a Posteriori Estimation
May 28, 2026Zhongling Wang, Raymond Zhou, Shahrukh Athar +2Image Quality Assessment MAP Estimation
University of Waterloo, Canada · McMaster University, Canada
3-D Relative Localization for Multi-Robot Systems with Angle and Self-Displacement Measurements
Apr 2, 2026Chenyang Liang, Liangming Chen, Baoyi Cui +1MAP Estimation Relative Pose Estimation
School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen, China. · School of Automation and Intelligent Manufacturing, and Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, Southern University of Science and Technology, China. · Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics, Harbin Institute of Technology, Shenzhen, China.
Spatiotemporal Calibration of Doppler Velocity Logs for Underwater Robots
Oct 28, 2025Hongxu Zhao, Guangyang Zeng, Yunling Shao +2Underwater Robotics MAP Estimation
The School of Data Science, Chinese University of Hong Kong, Shenzhen, Shenzhen, P. R. China.
A Morse-Bott Framework for Blind Inverse Problems: Local Recovery Guarantees and the Failure of the MAP
Aug 4, 2025Minh-Hai Nguyen, Edouard Pauwels, Pierre WeissImage Deblurring Bayesian Inference
IRIT, Toulouse University, Toulouse, France · Toulouse School of Economics, Universit´e Toulouse Capitole et IUF, Toulouse, France
ReMAP: Lossless Residual Neural Lifting for Scalable MAP Inference in Arbitrary-Order Markov Random Fields
Nov 28, 2024Yaomin Wang, Chaolong Ying, Xiaodong Luo +1MAP Estimation Markov Random Fields
School of Data Science, The Chinese University of Hong Kong, Shenzhen · Shenzhen Research Institute of Big Data, China

Sep 29, 2026J/K move · Enter open · S save

Principled MAP estimation for inverse problems: bridging the gap between convergence and performance

Alexandre Lagier, Valentine Tosel, Anne Gagneux, Mathurin Massias+1

ENS de Lyon, CNRS, Université Claude Bernard Lyon 1, Inria, LIP UMR 5668, 69342 Lyon Cedex 07, France · Univ. Bordeaux, Inria, Bordeaux INP, IMB, UMR 5251, F-33400 Talence, France · Inria, ENS de Lyon, CNRS, Université Claude Bernard Lyon 1, LIP UMR 5668, 69342 Lyon Cedex 07, France

Bayesian Inverse Problems Bayesian Inference Image Restoration Diffusion-Based Inverse Problems MAP Estimation Image Inverse Problems

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Pretrained denoisers provide a powerful way to incorporate image priors into restoration algorithms. Plug-and-Play and RED approaches exploit fixed-noise-level denoisers within first-order optimization schemes, with convergence guarantees, but often struggle to achieve high-quality reconstruction on severely ill-posed inverse problems. In contrast, recent state-of-the-art approaches leverage denoisers derived from flow- or diffusion-based generative models and evaluate them along a sequence of decreasing noise levels. While these methods achieve strong empirical performance, their convergence theory remains limited. In this paper, we bridge this gap by specifically designing an algorithm that combines denoisers at decreasing noise levels with a schedule tailored to ensure convergence. From a Bayesian perspective, we prove that our method converges to a

\textit{Maximum a Posteriori}

(MAP) estimate, under suitable assumptions. Subsequently, we apply our method to various ill-posed inverse problems and show that it surpasses convergent methods while competing with state-of-the-art empirical ones.