bayesian-psych
A library for easy creation of bayesian decision making models and classifiers
Availible Models:
- Inter Temporal Choice Heuristics (ITCH)
- Exponential Discounting
- Hyperbolic Discounting
- Quasi-Hyperbolic Discounting
Quick Example
Coming Soon
Data Column Meaning
| Column | Meaning |
|---|---|
| 1-p1 | one minus "more certain" outcome |
| 1-p2 | one minus "less certain" outcome |
| LL | TRUE = choose Larger Later reward FALSE = choose Smaller Sooner Reward |
| df_num | convenience variable distinguish each subject, discounting, reward case |
| discount_type | discount-type (i.e. time, probability, or effort) |
| e1 | smaller effort level |
| e2 | larger effort level |
| is_test | TRUE = data is part of testing set FALSE = data is part of training set |
| key | participant number + discount_type + reward_type |
| participant | participant number |
| reward_type | reward-type (i.e. social, health, money) |
| t1 | smaller time delay |
| t2 | larger time delay |
| x1 | smaller reward |
| x2 | larger reward |
| t1_n | smaller time delay normalized |
| t2_n | larger time delay normalized |
| x1_n | smaller reward normalized |
| x2_n | larger reward normalized |
Installation
git clone https://github.com/mhamilton723/bayesian-psych.git
cd bayesian-psych/src
python 2_model_comparison.pyIf you encounter errors, pip install the required packages. Run the code in the numerical order provided. You do not need to run 1_parse_raw_data.py since the full data set cannot be made publically available due to Institutional Review Board and HIPAA Privacy Rules.