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eess.SP updates on arXiv.org

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Monte-Carlo Sampling Approach to Model Selection: A Primer
Petre Stoica, Xiaolei Shang, Yuanbo Cheng · 2022-09-27 · via eess.SP updates on arXiv.org

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.

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