Inference tasks in signal processing are often characterized by the availability of reliable statistical modeling with some missing instance-specific parameters. One conventional approach uses data to estimate these missing parameters and then infers based on the estimated model. Alternatively, data can also be leveraged to directly learn the inference mapping end to end. These approaches for combining partially known statistical models and data in inference are related to the notions of generative and discriminative models used in the machine learning literature [1] , [2] , typically considered in the context of classifiers.

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随机信号线性估计的判别和生成学习 [讲座笔记]

信号处理中的推理任务通常以可靠的统计建模的可用性为特征，但缺少一些特定于实例的参数。一种传统方法使用数据来估计这些缺失的参数，然后根据估计的模型进行推断。或者，也可以利用数据来直接学习端到端的推理映射。这些在推理中组合部分已知的统计模型和数据的方法与机器学习文献中使用的生成模型和判别模型的概念相关。[1] ,[2] ，通常在分类器的上下文中考虑。