MAP Estimation

MAP: Maximum a Posteriori

Latest papers

10 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

D-MOD: Diffusion mode estimation for accelerated MRI
Oct 29, 2026Md Kawsar Ahmed, Mathews JacobAccelerated MRI Reconstruction Diffusion-Based Inverse Problems
University of Virginia, Charlottesville, VA, USA · Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
Efficient Deep Gaussian Process Surrogates via Mini-Batch Latent MAP Estimation
Oct 27, 2026Chun-Yi Chang, Chih-Li SungDeep Gaussian Processes Surrogate Modeling
Michigan State University
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

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D-MOD: Diffusion mode estimation for accelerated MRI

Md Kawsar Ahmed, Mathews Jacob

University of Virginia, Charlottesville, VA, USA · Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.

Accelerated MRI Reconstruction Diffusion-Based Inverse Problems MAP Estimation

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Diffusion models are powerful priors for accelerated MRI reconstruction, but most current methods are posterior samplers, which return a random draw from the posterior. A reproducible and high-quality reconstruction often requires averaging many samples, resulting in a high computational cost. We introduce D-MOD, a fast algorithm that instead provides a posterior mode. It uses a minorize-maximize algorithm on a lower bound of the smoothed log-posterior, which provably increases the objective. Each iteration draws S noisy samples of the current estimate at the current noise level, denoises them with a pretrained diffusion model, and combines their average with the measured k-space through a conjugate-gradient (CG) solve. We anneal the noise levels, as the distance between the bound and its target decays quadratically with noise. On fastMRI brain (T1, T2, FLAIR), a single D-MOD reconstruction exceeds, in PSNR, the 10-sample finite-MMSE estimates of DPS, DAPS and SNORE-P and stays within 0.31 dB of the strongest baselines (DiffPIR, DDS). This approach thus provides a fast deterministic reconstruction algorithm (11-73 times faster than MMSE estimates), which is useful in a clinical setting.