# IID Sampling from Intractable Multimodal and Variable-Dimensional Distributions

arXiv: https://arxiv.org/abs/2109.12633 · HTML: https://mlsanity.com/paper/2109.12633/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2021.

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

- [The Bayesian Reflex: Online Learning as the Autonomic Nervous System of Modern and Future AI](https://mlsanity.com/paper/2605.02825/) (2026)

## Abstract (arXiv)

Bhattacharya (2021b) has introduced a novel methodology for generating iid realizations from any target distribution on the Euclidean space, irrespective of dimensionality. In this article, our purpose is two-fold. We first extend the method for obtaining iid realizations from general multimodal distributions, and illustrate with a mixture of two 50-dimensional normal distributions. Then we extend the iid sampling method for fixed-dimensional distributions to variable-dimensional situations and illustrate with a variable-dimensional normal mixture modeling of the well-known "acidity data", with further demonstration of the applicability of the iid sampling method developed for multimodal distributions.