First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
PHYSICS OF DATA
Course unit
ADVANCED STATISTICS FOR PHYSICS ANALYSIS
SCP8082557, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
PHYSICS OF DATA
SC2443, Degree course structure A.Y. 2018/19, A.Y. 2018/19
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination ADVANCED STATISTICS FOR PHYSICS ANALYSIS
Website of the academic structure http://physicsofdata.scienze.unipd.it/2018/laurea_magistrale
Department of reference Department of Physics and Astronomy
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge ALBERTO GARFAGNINI FIS/01

ECTS: details
Type Scientific-Disciplinary 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

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019

Examination board
Examination board not defined

Syllabus
Prerequisites: None
Target skills and knowledge: Being able to solve statistical data analysis problems in the R framework.
Examination methods: Oral test examination.
A simple exercise, to be solved in the R framework, will be assigned few days before the oral examination.
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)

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Problem based learning
  • Interactive lecturing
  • Problem solving

Innovative teaching methods: Software or applications used
  • The R Project for Statistical Computing

Sustainable Development Goals (SDGs)
Quality Education Gender Equality