This is the final project for the course Bayesian Learning & MonteCarlo simulation attended at Politechnique of Milan during the accademic year 2022.
We have a dataset regarding the ”Vinho Verde” wine. In this dataset some physical measurements
taken from bottles of wine are combined with a sensory judgement about the quality (a vote 0 to 10)
of the wine itself.
The objective is to determine which factors influence the quality wine and to use the physical
measurements to correctly classify the wines in their quality category.
We use a Bayesian approach to find a model able to describe the relationship between the quality of the wine (target variable) and its features.
In order to do so, we use a Gibbs sampler such as JAGS.
- in the
\assignmentdirectory it is possible to find the different project proposals. We chose the 7th. - in the
\chainsdirectory we store the Markov chains we got using JAGS. Make sure to have this folder in you working directory. - in the
\datadirectory we store the dataset that needs to be analysed 1_data_analysis.Rmd: we make some exploratory analysis and make some considerations about the data.binomial_standardized.Rmd: we build a bayesian binomial regression with differente priors and make some predictions. Note: make sure to add\picturesfolder to your working directory, furthermore, you should add\pictures\binomial.spikeslab.rmd: we build a binomial regression with spikes and slab prior.softmax_standardized.Rmd: we build a bayesian categorical softmax classifier with Gaussian prior. Note: make sure to add\picturesfolder to your working directory, furthermore, you should add\pictures\categorical.utils_functions.R: is a file in which you can find some ggplot wrappers functions.
- R
- JAGS