# 🧠 EvoJump: Stochastic Modeling of Evolutionary Ontogenetic Trajectories

**Daniel A. Friedman** (2025) · *Zenodo*

[![DOI](https://img.shields.io/badge/DOI-10.5281%2Fzenodo.17229924-blue)](https://doi.org/10.5281/zenodo.17229924)

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## Abstract

> This paper presents EvoJump, a unified framework for stochastic modeling of evolutionary ontogenetic trajectories, aiming to enhance the understanding of biological processes through advanced statistical methods. The framework integrates various stochastic processes to analyze evolutionary patterns and dynamics effectively.

## Keywords

`evolutionary transitions` · `EvoJump` · `Active Inference` · `major transitions` · `phenotypic complexity` · `Free Energy Principle`

## Key Contributions

- Development of a unified framework for stochastic modeling in evolutionary biology.
- Integration of multiple stochastic processes, including Ornstein-Uhlenbeck and Lévy processes.
- Application of advanced statistical methods such as wavelet analysis and copula methods.
- Insights into evolutionary dynamics through case studies, including Drosophila analysis.

## Methods

- Utilization of stochastic process modeling techniques, including Ornstein-Uhlenbeck and Cox-Ingersoll-Ross processes.
- Implementation of wavelet analysis for multi-scale temporal patterns.
- Application of copula methods for analyzing trait dependencies.
- Use of Bayesian inference for parameter estimation and model validation.

## 🎯 Consulting & Tutoring

**Available for AI Research Consulting and Tutoring.** [Contact Daniel Ari Friedman, PhD](https://danielarifriedman.com/) for collaboration on Active Inference, Bayesian modeling, and computational biology.

## Citation

```bibtex
@article{2025_EvoJump,
  author = {Daniel A. Friedman},
  title = {{EvoJump: Stochastic Modeling of Evolutionary Ontogenetic Trajectories}},
  journal = {Zenodo},
  year = {2025},
  doi = {10.5281/zenodo.17229924},
}
```

## File Inventory

- `AGENTS.md` (1,810 bytes)
- `2025_EvoJump.pdf` (7,410,576 bytes)
- `README.md` (1,362 bytes)
- `SKILL.md` (1,709 bytes)