First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
STATISTICAL SCIENCES
Course unit
COMPUTATIONAL STATISTICS (ADVANCED)
SCP4063389, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
STATISTICAL SCIENCES
SS1736, Degree course structure A.Y. 2014/15, A.Y. 2018/19
N0
bring this page
with you
Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination COMPUTATIONAL STATISTICS (ADVANCED)
Website of the academic structure http://www.stat.unipd.it/studiare/ammissione-laurea-magistrale
Department of reference Department of Statistical Sciences
E-Learning website https://elearning.unipd.it/stat/course/view.php?idnumber=2018-SS1736-000ZZ-2017-SCP4063389-N0
Mandatory attendance No
Language of instruction Italian
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 MANUELA CATTELAN SECS-S/01

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-S/01 Statistics 9.0

Course unit organization
Period First semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Laboratory 3.0 22 53.0 No turn
Lecture 6.0 42 108.0 No turn

Calendar
Start of activities 01/10/2018
End of activities 18/01/2019
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
4 Commissione a.a.2018/19 01/10/2018 30/09/2019 CATTELAN MANUELA (Presidente)
GRIGOLETTO MATTEO (Membro Effettivo)
SCARPA BRUNO (Membro Effettivo)

Syllabus
Prerequisites: Contents of the courses: Calcolo delle Probabilità and Statistica progredito.
Useful, although not mandatory, Statistica Computazionale and basic knowledge of the software R.
Target skills and knowledge: - Development of new computational abilities for inference in statistical models.
- Learn how to implement such methods in R
Examination methods: Practical exam in computer lab. The exam includes both theoretical questions and empirical analyses.
Assessment criteria: The evaluation is based on completeness and correctness of the computer lab exam.
Course unit contents: - Simulation: rejection sampling; Monte Carlo integration; importance sampling and other variance reduction methods.
- Numerical and graphical methods for likelihood analysis and Bayesian inference.
- The EM algorithm.
- Resampling methods: bootstrap e jacknife.
- Markov chains and Markov Chain Monte Carlo (MCMC) methods: Markov chains theory; MCMC algorithms; applications to Bayesian inference.
Planned learning activities and teaching methods: Lectures and Computer Labs classes
Additional notes about suggested reading: Teaching material available on the course website
Textbooks (and optional supplementary readings)
  • Robert, Christian P.; Casella, George, Introducing Monte Carlo methods with R. New York: Springer, 2010. Cerca nel catalogo
  • Davison, Anthony Christopher; Hinkley, David V., Bootstrap methods and their application. Cambridge [etc.]: Cambridge university press, 1997. Cerca nel catalogo
  • Albert, Jim, Bayesian computation with R. Dordrecht: Springer Verlag, 2009. Cerca nel catalogo
  • Gelman, Andrew; Meng, Xiao-Li; Brooks, Steve; Jones, Galin L., Handbook of Markov Chain Monte Carlo. Boca Raton: CRC Press (Taylor & Francis Group), 2011. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)