A fast implementation of the Expected Value of Perfect Parameter Information (EVPPI) for large Monte Carlo simulations
Description
General Information
In this repository you can find 4 things:
- a Python/Numpy implementation in python
- a C implementation in c
- R bindings to the C implementation in r
- Python bindings to the C implementation using CFFI in python_cffi
The Expected Value of Perfect Parameter Information (EVPPI) is a concept from decision analysis (modeling decisions under uncertainty). It can be described as a measure for what a (rational) decision-maker would be willing to pay for zero uncertainty on a certain variable.
In general, the functions in this repository take in samples from a Monte Carlo model that predicts utility as a function of uncertain input parameters. Here, x denotes the values of the (uncertain) parameter inputs and y the resulting utility. More detailed documentation can be found in the respective packages.
Running the C implementation from R was found to be many times faster than existing R implementations, especially for a large number of Monte Carlo samples.
Version Information
First version. All 4 parts (C, Python, Python/CFFI and R) should be ready to be used in production.
Full Changelog: https://github.com/johanneskopton/evpi/commits/v1.0.0
Files
johanneskopton/evpi-v1.0.0.zip
Files
(42.4 kB)
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Additional details
Related works
- Is supplement to
- Software: https://github.com/johanneskopton/evpi/tree/v1.0.0 (URL)
Funding
- Deutsche Forschungsgemeinschaft
- PhenoRob EXC-2070–390732324
Software
- Repository URL
- https://github.com/johanneskopton/evpi
- Programming language
- C, R, Python