
Course unit
ADVANCED STATISTICS FOR PHYSICS ANALYSIS
SCP8082557, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2019/20
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Core courses 
FIS/01 
Experimental Physics 
6.0 
Course unit organization
Period 
Second semester 
Year 
1st Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Lecture 
6.0 
48 
102.0 
No turn 
Examination board
Board 
From 
To 
Members of the board 
2 Commissione Advanced Statistics for Physics analysis 2019/2020 
01/10/2019 
30/11/2020 
GARFAGNINI
ALBERTO
(Presidente)
ZANETTI
MARCO
(Membro Effettivo)
LIGUORI
MICHELE
(Supplente)

1 Commissione Advanced Statistics for Physics analysis 
01/10/2018 
30/11/2019 
GARFAGNINI
ALBERTO
(Presidente)
ZANETTI
MARCO
(Membro Effettivo)
LIGUORI
MICHELE
(Supplente)

Prerequisites:

None 
Target skills and knowledge:

Being able to solve statistical data analysis problems in the R framework. 
Examination methods:

A project will be assigned to students consisting in the statistical analysis of a physics dataset. The final exam will consist of the discussion of the project, its quality will determine the overall evaluation 
Assessment criteria:

Understanding the course topics and being able to solve practical problems with the R framework. 
Course unit contents:

 review of basic concepts: probability, odds and rules, updating probabilites, uncertain numbers (probability functions)
 from Bernoulli trials to Poisson processes and related distributions
 Bernoulli theorem and Central Limit Theorem
 Inference of the Bernoulli p; inference of lambda of the Poisson distribution. Inference of the Gaussian mu. Simultaneous inference of mu and sigma from a sample: general ideas and asymptotic results (large sample size).
 fits as special case of parametric inference
 Monte Carlo methods: rejecion sampling, inversion of cumulative distributions, importance sampling. Metropolis algorithm as example of Markov Chain Monte Carlo. Simulated annealing
 the R framework and language for applied statistics. 
Planned learning activities and teaching methods:

Lectures complemented by practical examples with laboratory exercises to be solved with the R analysis framework. 
Additional notes about suggested reading:

Lectures handouts. Reference material and textbooks will be given during the course. 
Textbooks (and optional supplementary readings) 

Sivia, Devinder S.; Skilling, John, Data analysisa Bayesian tutorialD. S. Sivia with J. Skilling. Oxford: O xford University press, 2006.

Innovative teaching methods: Teaching and learning strategies
 Lecturing
 Laboratory
 Problem based learning
 Case study
 Interactive lecturing
 Working in group
 Problem solving
Innovative teaching methods: Software or applications used
Sustainable Development Goals (SDGs)

