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

@article{Stoica2022TheMS, 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}, year={2022}, volume={39}, pages={85-92} }

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…

## One Citation

### Spectral information criterion for automatic elbow detection

- Computer Science
- 2022

A generalized information criterion which contains other well-known information criteria, such as BIC and AIC, as special cases, and provides a subset of all possible models, with a cardinality that often is much smaller than the total number of possible models.

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