Comparing Bayesian models in the absence of ground truth
Comparing Bayesian models in the absence of ground truth
复制标题
在没有基本事实的情况下比较贝叶斯模型
DOI：
10.1109/eusipco.2016.7760304
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
S. Mclaughlin
中科院分区：
文献类型：
--
作者：
M. Pereyra;S. Mclaughlin
现代信号处理方法强烈依赖贝叶斯统计模型来解决具有挑战性的问题。本文考虑了两种备选贝叶斯模型的客观比较，在没有基本事实的情况下，重点是模型的选择。现有的模型选择方法一般很难应用于信号处理，因为它们不适合具有不适当或模糊信息的先验的模型，并且因为与高维相关的挑战。本文提出了一种对高维、涉及适当、不适当或模糊先验的模型进行模型选择的一般方法。该方法基于一种包含两种模型的加性混合元模型表示，该元模型集中在最符合数据的模型上，并依赖于最近的马尔可夫链蒙特卡罗算法来高效地执行高维计算。该方法在一系列与全变差先验的图像分辨率增强相关的实验中得到了验证。
Modern signal processing methods rely strongly on Bayesian statistical models to solve challenging problems. This paper considers the objective comparison of two alternative Bayesian models, for scenarios with no ground truth available, and with a focus on model selection. Existing model selection approaches are generally difficult to apply to signal processing because they are unsuitable for models with priors that are improper or vaguely informative, and because of challenges related to high dimensionality. This paper presents a general methodology to perform model selection for models that are high-dimensional and that involve proper, improper, or vague priors. The approach is based on an additive mixture meta-model representation that encompasses both models and which concentrates on the model that fits the data best, and relies on proximal Markov chain Monte Carlo algorithms to perform high-dimensional computations efficiently. The methodology is demonstrated on a series of experiments related to image resolution enhancement with a total-variation prior.