论文标题
部分可观测时空混沌系统的无模型预测
Monte-Carlo Sampling Approach to Model Selection: A Primer
论文作者
论文摘要
任何数据建模练习都有两个主要组成部分：参数估计和模型选择。后者将是本讲座的主题。更具体地说，我们将使用最大A后验（MAP）方法介绍几个基于蒙特卡洛抽样的规则，以进行模型选择。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变量的顺序，通信通道的脉冲响应的长度，撞击传感器阵列的源信号的数量，多项式趋势的顺序，多项式趋势的顺序，NMR信号的组件数量等等。
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.
