causact: R package to accelerate computational Bayesian inference workflows in R through interactive visualization of models and their output.
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Updated
Apr 24, 2024 - R
causact: R package to accelerate computational Bayesian inference workflows in R through interactive visualization of models and their output.
R package for inference in Bayesian networks.
Splotch is a hierarchical generative probabilistic model for analyzing Spatial Transcriptomics (ST) data
dbnlearn: An R package for Dynamic Bayesian Network Structure Learning, Parameter Learning and Forecasting
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
Inference in Bayesian Networks with R
Weather Generators with Bayesian Networks
bnviewer - An R package for Interactive Visualization of Bayesian Networks
Gaussian Mixture Graphical Model Learning and Inference
Constructing a Bayesian network to capture the dependencies and independencies among variables as well as to predict wine quality
This project provide a new method to infer the causal structure among genes. Characterize genes into Causal/effect genes.
The Inductive Causation and IC* algorithms applied to a fake data set
The JAGS Module
Files for PM project and exam
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
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