First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
STATISTICAL SCIENCES
Course unit
STOCHASTIC PROCESSES
SCP4063083, A.A. 2017/18

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. 2017/18
N0
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Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination STOCHASTIC PROCESSES
Website of the academic structure http://scienzestatistiche.scienze.unipd.it/2017/laurea_magistrale
Department of reference Department of Statistical Sciences
E-Learning website https://elearning.unipd.it/stat/course/view.php?idnumber=2017-SS1736-000ZZ-2017-SCP4063083-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 MARCO FERRANTE MAT/06

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
SCO2046352 INTRODUCTION TO STOCHASTIC PROCESSES MARCO FERRANTE SC1172

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines MAT/06 Probability and Mathematical Statistics 9.0

Course unit organization
Period First semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 9.0 64 161.0 No turn

Calendar
Start of activities 02/10/2017
End of activities 19/01/2018
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
5 Commissione a.a.2018/19 01/10/2018 30/09/2019 FORMENTIN MARCO (Presidente)
BARBATO DAVID (Membro Effettivo)
CELANT GIORGIO (Membro Effettivo)
CESARONI ANNALISA (Membro Effettivo)
4 Commissione a.a. 2017/18 01/10/2017 30/09/2018 FERRANTE MARCO (Presidente)
BARBATO DAVID (Membro Effettivo)
DAI PRA PAOLO (Membro Effettivo)

Syllabus
Prerequisites: A basic course in Probability
Target skills and knowledge: Good knowledge of the theory of the discrete time- and continuous time Markov chains, and ability to solve also advanced problems and exercises related to these processes.
Examination methods: Written examination
Assessment criteria: Homeworks (10%) - Final Exam (90%)
Course unit contents: Definition of Stochastic process. Probability and conditional expectation. Conditional independence.
Discrete-time Markov chains: basic definitions, transition matrix, Markov property, Random Walk and its properties, absorption probabilities, stopping times, strong Markov property, classification of the states,
periodicity, invariant distributions, Ergodic theorem.
Poisson process: main properties and applications.
Continuous-time Markov chains: basic definitions, generator matrix, Jump chain and holding times, absorption probabilities, classification of the states, invariant distribution, Ergodic theorem.
Applications: Birth and death process, Queues and queueing networks, Stochastic models in sport and Information Retrieval
Planned learning activities and teaching methods: Taught lessons: theory (34 hours) exercises (30 hours)
Textbooks (and optional supplementary readings)
  • J.Norris, Markov Chains. Cambridge: Cambridge University Press, 1996. Cerca nel catalogo
  • Paolo Baldi, Calcolo delle probabilit√† (2 ed.). Milano: McGraw-Hill, 2011. Cerca nel catalogo