This unit provides a strong background in the analysis of multivariate and categorical data. Concepts such as probability theory, Bayesian modelling, dimensionality reduction, clustering, finite mixture modelling and probabilistic graphical models form the core knowledge of this unit.
Causal discovery methods: simulation study and empirical applications
This project provide a new method to infer the causal structure among genes. Characterize genes into Causal/effect genes.
Files for PM project and exam
The JAGS Module
Constructing a Bayesian network to capture the dependencies and independencies among variables as well as to predict wine quality
The Inductive Causation and IC* algorithms applied to a fake data set
Gaussian Mixture Graphical Model Learning and Inference
Weather Generators with Bayesian Networks
bnviewer - An R package for Interactive Visualization of Bayesian Networks
Inference in Bayesian Networks with R
My version of topic modelling using Latent Dirichlet Allocation (LDA) which finds the best number of topics for a set of documents using ldatuning package which comes with different metrics
dbnlearn: An R package for Dynamic Bayesian Network Structure Learning, Parameter Learning and Forecasting
R package for inference in Bayesian networks.
causact: R package to accelerate computational Bayesian inference workflows in R through interactive visualization of models and their output.
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