Inference of Diffusive Model Dynamics in Sparse Data Regimes through Simulation-Based Methods

This repository contains code and notebooks for simulating and analyzing diffusive model dynamics using simulation-based inference (SBI) methods.

Notebooks

• Brownian_Motion_SBI_embedding.ipynb: Simulates Brownian motion and uses SBI for embedding.
• Brownian_Motion_SBI_transition_matrix.ipynb: Simulates Brownian motion and uses SBI with a transition matrix.
• joint_posterior_advanced_metrics.ipynb: Analyzes joint posterior distributions with advanced metrics.
• langevin_integrator_SBI.ipynb: Simulates Langevin dynamics and uses SBI.
• plotting_code.ipynb: Contains code for plotting results.
• posterior_missspecification.ipynb: Analyzes posterior misspecification.
• potential_plot.ipynb: Plots potential functions.
• sbc.ipynb: Performs simulation-based calibration (SBC).

Source Code

• src/mamba.py: Contains the implementation of the Mamba model.
• src/pscan.py: Contains the implementation of the pscan algorithm.
• src/temporal_encoders.py: Contains temporal encoding functions.

Getting Started

1. Clone the repository:

   git clone [https://github.com/yourusername/your-repo.git](https://github.com/atemynany/Inference-of-Diffusive-Model-Dynamics-in-Sparse-Data-Regimes-through-Simulation-Based-Methods/new/main?filename=README.md)
   cd your-repo

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the notebooks using Jupyter:

   jupyter notebook