Prof. Alessandra Guglielmi

Argomenti del programma: The first part of the course (topic 1) will enable students to understand foundational aspects of Bayesian Statistics (the choice of the prior, for instance), while in the second part (topics 2-7), after the introduction of Markov Chain Monte Carlo techniques for simulation from the posterior, students will be able to use specific statistical models for estimation, prediction or clustering. 1.

Argomenti del programma: The first part of the course (topic 1) will enable students to understand foundational aspects of Bayesian Statistics (the choice of the prior, for instance), while in the second part (topics 2-7), after the introduction of Markov Chain Monte Carlo techniques for simulation from the posterior, students will be able to use specific statistical models for estimation, prediction or clustering. 1.

• A cura dei docenti, Appunti di lezione, esercitazione e laboratorio, temi d'esame

Argomenti del programma: Elementi di calcolo delle probabilità e variabili aleatorie. Esperimenti aleatori, variabili aleatorie (va) e probabilità. Va discrete e continue: densità, funzione di ripartizione, media, varianza, standardizzazione di una va. Distribuzioni notevoli: bernoulliana, binomiale, di Poisson, uniforme, esponenziale, normale (gaussiana); loro principali proprietà e applicazioni.

Argomenti del programma: The first part of the course (topic 1) will enable students to understand foundational aspects of Bayesian Statistics (the choice of the prior, for instance), while in the second part (topics 2-7), after the introduction of Markov Chain Monte Carlo techniques for simulation from the posterior, students will be able to use specific statistical models for estimation, prediction or clustering. 1.

Argomenti del programma: The first part of the course (topic 1) will enable students to understand foundational aspects of Bayesian Statistics (the choice of the prior, for instance), while in the second part (topics 2-7), after the introduction of Markov Chain Monte Carlo techniques for simulation from the posterior, students will be able to use specific statistical models for estimation, prediction or clustering. 1.