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https://github.com/mhamilton723/bayesian-psych
A library for comparing and developing Bayesian models of human decision making
https://github.com/mhamilton723/bayesian-psych
Last synced: about 14 hours ago
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A library for comparing and developing Bayesian models of human decision making
- Host: GitHub
- URL: https://github.com/mhamilton723/bayesian-psych
- Owner: mhamilton723
- Created: 2017-07-19T01:13:33.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2017-10-05T01:24:08.000Z (over 7 years ago)
- Last Synced: 2024-11-23T08:25:20.519Z (2 months ago)
- Language: Python
- Homepage:
- Size: 12.3 MB
- Stars: 2
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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
```bash
git clone https://github.com/mhamilton723/bayesian-psych.git
cd bayesian-psych/src
python 2_model_comparison.py
```If 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.