https://github.com/dirmeier/bayesian-networks-introduction
:bar_chart: Introduction to Bayesian Networks.
https://github.com/dirmeier/bayesian-networks-introduction
bayesian-networks tutorial
Last synced: 10 months ago
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:bar_chart: Introduction to Bayesian Networks.
- Host: GitHub
- URL: https://github.com/dirmeier/bayesian-networks-introduction
- Owner: dirmeier
- Created: 2020-01-13T11:42:18.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-01-17T19:32:41.000Z (about 6 years ago)
- Last Synced: 2025-04-15T16:59:49.807Z (10 months ago)
- Topics: bayesian-networks, tutorial
- Language: R
- Homepage: https://dirmeier.github.io/bayesian-networks-introduction/index.html
- Size: 11.4 MB
- Stars: 5
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
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README
---
output: md_document
---
# Introduction to Bayesian networks in R
[](https://www.repostatus.org/#concept)
## About
This repository contains a practical introduction to *Bayesian networks* in `R` using `bnlearn` for teaching of a one-hour course.
```{r plot, echo=FALSE, message=FALSE, warning=FALSE, fig.align = "center", fig.width=7, fig.height=4}
library(dagitty)
library(bnlearn)
source(file.path(here::here(), "R", "_plot_boostrapped_graph.R"))
signalling.data <- readRDS(
file.path(here::here(), "data", "signalling_data.rds"))
boot.tabu <- boot.strength(signalling.data, algorithm="tabu")
plot.bootstrapped(boot.tabu)
```
## Usage
To go through the `R` tutorials do the following:
- download the latest zip-file [here](https://github.com/dirmeier/bayesian-networks-introduction/releases) and extract it,
- open *Rstudio*,
- click *File -> Open Project* and select `bayesian-networks-introduction.Rproj`,
- open `R/tutorial.R`,
- execute the tutorial line by line.
## Session info
```{r, echo=FALSE}
sessionInfo()
```
## Author
Simon Dirmeier simon.dirmeier @ web.de
## License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.