# Simulating signed mixtures

arXiv: https://arxiv.org/abs/2401.16828 · HTML: https://mlsanity.com/paper/2401.16828/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2024.

## Built on by (most cited first, 2 of 2)

- [How to Approximate Inference with Subtractive Mixture Models](https://mlsanity.com/paper/2604.16714/) (2026)
- [Scalable Expectation Estimation with Subtractive Mixture Models](https://mlsanity.com/paper/2503.21346/) (2025)

## Abstract (arXiv)

Simulating mixtures of distributions with signed weights proves a challenge as standard simulation algorithms are inefficient in handling the negative weights. In particular, the natural representation of mixture variates as associated with latent component indicators is no longer available. We propose here an exact accept-reject algorithm in the general case of finite signed mixtures that relies on optimaly pairing positive and negative components and designing a stratified sampling scheme on pairs. We analyze the performances of our approach, relative to the inverse cdf approach, since the cdf of the distribution remains available for standard signed mixtures.