# Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra

arXiv: https://arxiv.org/abs/2108.08709 · HTML: https://mlsanity.com/paper/2108.08709/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation](https://mlsanity.com/paper/2001.08049/) (2020)
- [Deep Spectral CNN for Laser Induced Breakdown Spectroscopy](https://mlsanity.com/paper/2012.01653/) (2020)

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

- [Generative structured normalizing flow Gaussian processes applied to spectroscopic data](https://mlsanity.com/paper/2212.07554/) (2022)

## Abstract (arXiv)

Constructing probability densities for inference in high-dimensional spectral data is often intractable. In this work, we use normalizing flows on structured spectral latent spaces to estimate such densities, enabling downstream inference tasks. In addition, we evaluate a method for uncertainty quantification when predicting unobserved state vectors associated with each spectrum. We demonstrate the capability of this approach on laser-induced breakdown spectroscopy data collected by the ChemCam instrument on the Mars rover Curiosity. Using our approach, we are able to generate realistic spectral samples and to accurately predict state vectors with associated well-calibrated uncertainties. We anticipate that this methodology will enable efficient probabilistic modeling of spectral data, leading to potential advances in several areas, including out-of-distribution detection and sensitivity analysis.