# You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/you-only-derive-once-yodo-automatic-differentiation-for-efficient-sensitivity/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Rafael Ballester‐Ripoll,Manuele Leonelli |
| Citations | 10 |
| DOI | 10.48550/arxiv.2206.08687 |
| Fields | Computer Science,Decision Sciences,Mathematics |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/2206.08687 |
| OpenAlex ID | https://openalex.org/W4283214680 |
| Type | preprint |
| Year | 2022 |

## Paper authors

- [Manuele Leonelli](https://scholariq.org/researchers/manuele-leonelli/)

## Paper journal

- [arXiv (Cornell University)](https://scholariq.org/journals/arxiv-cornell-university/)

## Paper primary topic

- [Bayesian Modeling and Causal Inference](https://scholariq.org/topics/bayesian-modeling-and-causal-inference/)

## Paper topics

- [Bayesian Modeling and Causal Inference](https://scholariq.org/topics/bayesian-modeling-and-causal-inference/)
- [Statistical Methods and Bayesian Inference](https://scholariq.org/topics/statistical-methods-and-bayesian-inference/)
- [Data Quality and Management](https://scholariq.org/topics/data-quality-and-management/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.