Final project of the course "Statisical Methods in Data Science II" of the Master Degree in Data Science at Sapienza University of Rome.
The project is a fully Bayesian analysis using MCMC of real time-series data obtained from Google Trends .
In the project the steps are considered:
- illustration of the dataset
- explanation of the overall features of the statistical model, such as the role of the parameters and the inferential goals of the analysis
- illustration of the main inferential findings (Bayesian point and interval estimation, hypothesis testing)
- discussion of one possible alternative statistical model and showcase of the results of model comparison through DIC
- illustration of the features of the MCMC convergence diagnostics and error control
Link to the report: https://rawcdn.githack.com/bergio13/bayesian_inference_mcmc/d070625cea8e6650dee232aaf4cb2a3a1a9b701f/final_proj_2.html