The Monte-Carlo Sampling Approach to Model Selection: A Primer [Lecture Notes]

  title={The Monte-Carlo Sampling Approach to Model Selection: A Primer [Lecture Notes]},
  author={Petre Stoica and Xiaolei Shang and Yuanbo Cheng},
  journal={IEEE Signal Processing Magazine},
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 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… 

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