LM Data Science
STATISTICAL METHODS IN DATA SCIENCE AND LABORATORY II
Luca Tardella
The registration key will be delivered to all students during the first lecture. If you miss it please send an email to luca.tardella@uniroma1.it
A comprehensive overview as a pdf file can be downloaded from here: https://drive.google.com/file/d/1eDOH0EmjWIR2A-Lkc9BHgEg3gnELmqgh
This
is an introductory course about Bayesian inference and Bayesian
modelling for data analysis. We will balance between theoretical and
analytical tools and practice. In particular for practical
implementation of Bayesian models on real data we will make use of some
software for Bayesian modelling and inference (R, BUGS/JAGS, INLA).
is an introductory course about Bayesian inference and Bayesian
modelling for data analysis. We will balance between theoretical and
analytical tools and practice. In particular for practical
implementation of Bayesian models on real data we will make use of some
software for Bayesian modelling and inference (R, BUGS/JAGS, INLA).
In this course we will cover:
- Introduction to Bayesian Thinking
In this part we will
cover basic definitions, Bayesian model, Bayes’ theorem. Subjective
prior elicitation and some noninformative or default prior choices (e.g.
Jeffreys’Rule). Conjugate analysis. Techniques and tools for
characterizing and summarizing posterior distributions. Bayes Factor, introduction to multi-model inference and Bayesian model choice. - Multiparameter Inference
In this part we will cover
multivariate normal and multinomial models and introduces to approximate
random sampling from a multivariate distribution. Bayesian inference in
the presence of missing data. - MC & MCMC
Introduction to Monte Carlo and Monte
Carlo Markov Chain sampling techniques for approximating expectations
and distributions: Gibbs Sampling and Metropolis Hastings algorithms.
- Teacher: Pierfrancesco Alaimo Di Loro
- Teacher: LUCA Tardella