New Loss Functions in Bayesian Imaging
本文针对贝叶斯成像中损失函数设计被忽视的问题，提出具有局部结构的损失函数，并用马尔可夫链蒙特卡洛和模拟退火算法计算贝叶斯估计，在伊辛模型上进行了仿真实验。
Abstract Unlike the development of more accurate prior distributions for use in Bayesian imaging, the design of more sensible estimators through loss functions has been neglected in the literature. We discuss the design of loss functions with a local structure that depend only on a binary misclassification vector. The proposed approach is similar to modeling with a Markov random field. The Bayes estimate is calculated in a two-step algorithm using Markov chain Monte Carlo and simulated annealing algorithms. We present simulation experiments with the Ising model, where the observations are corrupted with Gaussian and flip noise.