#### 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.itA comprehensive overview as a pdf file can be downloaded from here: bit.ly/SMDS-2-2024

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).

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).

In this course we will cover:

**Introduction to Bayesian Thinking***In this part we will*

cover basic definitions, Bayesian model, Bayesâ€™ theorem. Subjective

prior elicitation and some noninformative or default prior choices (e.g.

Jeffreysâ€™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***This part considers the multivariate normal model and multinomial models and introduce 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: marco aurelio sterpa
- Teacher: Luca Tardella