- Dynamic Bayesian network of dermatologic and mental conditions in Scutari, Kerob and
Salah (in the works).
- The Data: an Overview
- Dependence Structure of the Data
- Bayesian Networks: Static versus Dynamic Structures
- Learning the Dynamic Bayesian Network
- Validating Predictive Accuracy

- A network analysis of empathy and self-worth, Psychological Reports (2024).
- Loading and preparing the data
- Learning a causal network with model averaging

- Structure learning benchmarks in Scutari, Marquis and Azzimonti, Proceedings of Machine
Learning Research (2022)
- Generating the ground-truth models
- Scoring local distributions that are linear mixed-effects models
- Learning the different types of networks
- Comparing the learned networks

- A network analysis of the symptoms from the Zung depression scale components,
Psychological Reports (2020)
- Loading the required libraries
- Loading and exploring the data
- Learning an undirected network model
- Learning a directed network model
- A network comparison test

- Structure learning benchmarks in Scutari, Graafland and Gutiérrez,
International Journal of Approximate Reasoning (2019)
- The
`makefile`

- Generating the data
- Performing structure learning
- Collecting summary statistics
- Generating the figures

- The
- Benchmarking optimizations in Scutari, Vitolo and Tucker, Statistics and Computing (2019)
- Benchmarks on the MEHRA network
- Benchmarks on other large data sets

- Analysis of pollution, climate and health data in Vitolo et al., Earth and Space Science (2018)
- The data
- Learning the Bayesian network

- Analysis of class III malocclusion in Scutari et al., Scientific Reports (2017)
- The data
- Preprocessing and exploratory data analysis
- Learning the Bayesian network
- Model validation
- Interesting questions

- Parallel structure learning benchmarking in Scutari, Journal of Statistical Software (2017)
- Parallel computing benchmarks
- Symmetry correction benchmarks

- Structure learning benchmarks in Scutari, Journal of Machine Learning Research (2016)
- Learning the network structures
- Computing the metrics of interest to compare different network scores
- Computing the averaged metrics for each network and sample size
- Plotting the metrics of interest, for visual inspection
- Plotting the metrics of interest, for slides presentations

- Analysis of the MAGIC population in Scutari
*et al.*, Genetics (2014)- Reading and preparing the preprocessed data
- Performing cross-validation
- Averaging the network structures
- Plotting the averaged network

- Reproducing the causal signalling network in Sachs
*et al.*, Science (2005)- The raw data
- Model averaging
- Choosing the Significance Threshold
- Handling Interventional Data
- Querying the Network