# Monte-Carlo Sampling Approach to Model Selection: A Primer

arXiv: https://arxiv.org/abs/2209.13203 · HTML: https://mlsanity.com/paper/2209.13203/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2022.

## Built on by (most cited first, 1 of 1)

- [Spectral information criterion for automatic elbow detection](https://mlsanity.com/paper/2308.09108/) (2023)

## Abstract (arXiv)

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.