Monte-Carlo Sampling Approach to Model Selection: A Primer
Methodology
2022-09-28 v1 Signal Processing
Abstract
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.
Cite
@article{arxiv.2209.13203,
title = {Monte-Carlo Sampling Approach to Model Selection: A Primer},
author = {Petre Stoica and Xiaolei Shang and Yuanbo Cheng},
journal= {arXiv preprint arXiv:2209.13203},
year = {2022}
}