# Nested Inheritance Dynamics

arXiv: https://arxiv.org/abs/2404.17601 · HTML: https://mlsanity.com/paper/2404.17601/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 1 of 1)

- [Bayesian Nonparametrics: An Alternative to Deep Learning](https://mlsanity.com/paper/2404.00085/) (2024)

## Abstract (arXiv)

The idea of the inheritance of biological processes, such as the developmental process or the life cycle of an organism, has been discussed in the biology literature, but formal mathematical descriptions and plausible data analysis frameworks are lacking. We introduce an extension of the nested Dirichlet Process (nDP) to a multiscale model to aid in understanding the mechanisms by which biological processes are inherited, remain stable, and are modified across generations. To address these issues, we introduce Nested Inheritance Dynamics Algorithm (NIDA). At its primary level, NIDA encompasses all processes unfolding within an individual organism's lifespan. The secondary level delineates the dynamics through which these processes evolve or remain stable over time. This framework allows for the specification of a physical system model at either scale, thus promoting seamless integration with established models of development and heredity.