Active Inference · Paper · 2025

EvoJump: Stochastic Modeling of Evolutionary Ontogenetic Trajectories

Zenodo

Catalog Row12
Citation KeyFriedman2025EvoJumpStochasticModelingEvolutionary012
Paper FolderAvailable
DOI10.5281/zenodo.17229924

Overview

Extracted from the local paper documentation when available.

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.

evolutionary transitionsEvoJumpActive Inferencemajor transitionsphenotypic complexityFree Energy Principle

Use Notes

Concise findings and methods pulled from README/SKILL documentation.

Findings / Concepts
  • 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 / Techniques
  • 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.

Citation

Plain-text citation for quick reuse.

Friedman, Daniel Ari. 2025. EvoJump: Stochastic Modeling of Evolutionary Ontogenetic Trajectories. Zenodo.

Primary source Documentation BibTeX