# Quantifying Context Effects in a Survey with Hierarchical Modeling

We use a Bayesian Hierarchical Model for modeling discrete choice in a similarity setting.


### TODO modules

- Model Specification
- Model fitting
- Survey data preprocessing
- Synthetic Data*
- Posterior Predictive Checks
    - User dependence
    - Context dependence
- Plotting
    - Parameter Posteriors
    - Predictive Simulations


*In our context, the synthetic data will be used for model validation, not to improve training.


## installation

Due to PyMC, we recommend using conda and conda environments for installing the required Python libraries.

Per the [PyMC documentation](https://www.pymc.io/projects/docs/en/latest/installation.html), start with

```
conda create -c conda-forge -n pymc_env "pymc>=5"
conda activate pymc_env
```

And then perform

```
pip install -r requirements.txt
```