Any data modeling exercise has two main components: parameter estimation and model selection. The latter will be the topic of this lecture note. More concretely we will introduce several Monte-Carlo sampling-based rules for model selection using the maximum a posteriori (MAP) approach. Model selection problems are omnipresent in signal processing applications: examples include selecting the order of an autoregressive predictor, the length of the impulse response of a communication channel, the number of source signals impinging on an array of sensors, the order of a polynomial trend, the number of components of a NMR signal, and so on.

中文翻译：

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模型选择的蒙特卡罗抽样方法：入门

任何数据建模练习都有两个主要部分：参数估计和模型选择。后者将是本讲稿的主题。更具体地说，我们将介绍几个基于蒙特卡罗采样的规则，用于使用最大后验 (MAP) 方法进行模型选择。模型选择问题在信号处理应用中无处不在：示例包括选择自回归预测器的阶数、通信通道的脉冲响应长度、冲击传感器阵列的源信号的数量、多项式趋势的阶数、 NMR 信号的分量数等。