By name: "EvoJump" description: "Proficient in stochastic modeling and advanced statistical analysis for evolutionary biology, utilizing frameworks like EvoJump to derive insights from complex biological data." tags: ["active inference", "evolutionary-transitions", "evojump", "active-inference", "major-transitions", "phenotypic-complexity"]
EvoJump: Stochastic Modeling of Evolutionary Ontogenetic Trajectories
Daniel A. Friedman (2025) · Active Inference
Instructions
Use this skill when working with topics related to evolutionary transitions, EvoJump, Active Inference, major transitions.
When applying this skill:
- Model evolutionary transitions using Active Inference
- Analyze major transitions in biological complexity through FEP
Key Concepts
- evolutionary transitions
- EvoJump
- Active Inference
- major transitions
- phenotypic complexity
- Free Energy Principle
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.
Key Findings
- Demonstrated the effectiveness of the EvoJump framework in modeling complex evolutionary trajectories.
- Identified long-range temporal dependencies in evolutionary processes.
- Provided evidence of selective sweeps and genetic hitchhiking in Drosophila populations.
- Validated the framework through extensive simulations and statistical methods.
Prerequisites
- Probability theory and Bayesian inference basics
- Free Energy Principle and generative models
🎯 Consulting & Tutoring
Daniel Ari Friedman, PhD is available for AI Research Consulting and Tutoring related to this skill.
Related Skills
See BIBLIOGRAPHY.md for the complete publication catalog and related papers.