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https://github.com/madsjulia/biguq.jl
Bayesian Information Gap Decision Theory
https://github.com/madsjulia/biguq.jl
bayesian bayesian-data-analysis data-driven data-science decision-analysis decision-making decision-model decision-support decision-theory experimental-design high-performance-computing information-gap information-theory julia mads model-analysis model-driven predictive-analysis uncertainty-quantification
Last synced: 7 days ago
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Bayesian Information Gap Decision Theory
- Host: GitHub
- URL: https://github.com/madsjulia/biguq.jl
- Owner: madsjulia
- License: gpl-3.0
- Created: 2016-01-26T14:34:19.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2023-12-15T20:59:49.000Z (11 months ago)
- Last Synced: 2024-08-01T03:40:56.003Z (4 months ago)
- Topics: bayesian, bayesian-data-analysis, data-driven, data-science, decision-analysis, decision-making, decision-model, decision-support, decision-theory, experimental-design, high-performance-computing, information-gap, information-theory, julia, mads, model-analysis, model-driven, predictive-analysis, uncertainty-quantification
- Language: Julia
- Homepage: http://mads.gitlab.io
- Size: 99.6 KB
- Stars: 18
- Watchers: 11
- Forks: 4
- Open Issues: 3
-
Metadata Files:
- Readme: Readme.md
- License: LICENSE
Awesome Lists containing this project
README
# BIGUQ
BIGUQ performs Bayesian Information Gap Decision Theory (BIG-DT) analysis for Uncertainty Quantification, Experimental Design, and Decision Analysis.
BIGUQ is a module of [MADS](http://madsjulia.github.io/Mads.jl).
Example
-------
```julia
import Mads
problemdir = Mads.getmadsdir()
md = Mads.loadmadsfile(joinpath(problemdir, "source_termination.mads"))
nsample = 1000
bigdtresults = Mads.dobigdt(md, nsample; maxHorizon=0.8, numlikelihoods=5)
Mads.plotrobustnesscurves(md, bigdtresults; filename=joinpath(problemdir, "source_termination-robustness-$nsample"))
Mads.plotrobustnesscurves(md, bigdtresults; filename=joinpath(problemdir, "source_termination-robustness-zoom-$nsample"), maxhoriz=0.4, maxprob=0.1)
```
MADS
====
[MADS](http://madsjulia.github.io/Mads.jl) (Model Analysis & Decision Support) is an integrated open-source high-performance computational (HPC) framework in [Julia](http://julialang.org).
MADS can execute a wide range of data- and model-based analyses:
* Sensitivity Analysis
* Parameter Estimation
* Model Inversion and Calibration
* Uncertainty Quantification
* Model Selection and Model Averaging
* Model Reduction and Surrogate Modeling
* Machine Learning and Blind Source Separation
* Risk Assessment
* Decision Analysis and Support
MADS has been tested to perform HPC simulations of a wide range of multi-processor clusters and parallel environments (Moab, Slurm, etc.).
MADS utilizes adaptive rules and techniques which allows the analyses to be performed with a minimum user input.
The code provides a series of alternative algorithms to execute each type of data- and model-based analysis.
Documentation
=============
All the available MADS modules and functions are described at [madsjulia.github.io](http://madsjulia.github.io/Mads.jl)
Installation
============
```julia
Pkg.add("Mads")
```
Installation behind a firewall
------------------------------
Julia uses git for package management.
To install Julia packages behind a firewall, add the following lines in the `.gitconfig` file in your home directory:
```git
[url "https://"]
insteadOf = git://
```
or execute:
```bash
git config --global url."https://".insteadOf git://
```
Set proxies:
```bash
export ftp_proxy=http://proxyout.:8080
export rsync_proxy=http://proxyout.:8080
export http_proxy=http://proxyout.:8080
export https_proxy=http://proxyout.:8080
export no_proxy=.
```
For example, if you are doing this at LANL, you will need to execute the
following lines in your bash command-line environment:
```bash
export ftp_proxy=http://proxyout.lanl.gov:8080
export rsync_proxy=http://proxyout.lanl.gov:8080
export http_proxy=http://proxyout.lanl.gov:8080
export https_proxy=http://proxyout.lanl.gov:8080
export no_proxy=.lanl.gov
```
MADS examples
=============
In Julia REPL, do the following commands:
```julia
import Mads
```
To explore getting-started instructions, execute:
```julia
Mads.help()
```
There are various examples located in the `examples` directory of the `Mads` repository.
For example, execute
```julia
include(Mads.madsdir * "/../examples/contamination/contamination.jl")
```
to perform various example analyses related to groundwater contaminant transport, or execute
```julia
include(Mads.madsdir * "/../examples/bigdt/bigdt.jl")
```
to perform Bayesian Information Gap Decision Theory (BIG-DT) analysis.
Developers
==========
* [Velimir (monty) Vesselinov](https://montyv.github.io) [(publications)](http://scholar.google.com/citations?user=sIFHVvwAAAAJ)
* [Daniel O'Malley](http://www.lanl.gov/expertise/profiles/view/daniel-o'malley) [(publications)](http://scholar.google.com/citations?user=rPzCVjEAAAAJ)
Projects:
---------
* [MADS](https://github.com/madsjulia)
* [SmartTensors](https://github.com/SmartTensors)
* [SmartML](https://github.com/SmartTensors/SmartML.jl)
Publications, Presentations
--------------------------
* [mads.gitlab.io](http://mads.gitlab.io)
* [madsjulia.github.io](https://madsjulia.github.io)
* [SmartTensors](https://SmartTensors.github.io)
* [SmartTensors.com](https://SmartTensors.com)
* [monty.gitlab.io](http://monty.gitlab.io)
* [montyv.github.io](https://montyv.github.io)