Training and Research
PhD Programme Courses/classes - 2022/2023
Advice to Young Economists
Credits: 4
Language: English
Teacher: Marco Piovesan
Behavioral and Experimental Economics
Credits: 5
Language: Italian
Teacher: Simone Quercia, Maria Vittoria Levati, Marco Piovesan
Development Economics
Credits: 5
Language: English
Teacher: Federico Perali
Finance
Credits: 5
Language: English
Teacher: Cecilia Mancini
Game Theory
Credits: 5
Language: Inglese
Teacher: Francesco De Sinopoli
Inequality
Credits: 5
Language: English
Teacher: Francesco Andreoli, Claudio Zoli
Introduction to Probability – Module II (attività formativa per la Scuola di Dottorato)
Credits: 2
Language: Italian
Teacher: Claudia Di Caterina
Introduction to Probability – Module I
Credits: 2
Language: English
Introduction to Statistical Inference
Credits: 2
Language: English
Teacher: Marco Minozzo
Macroeconomics I
Credits: 7,5
Language: English
Teacher: Tamara Fioroni, Alessia Campolmi
Mathematics
Credits: 7,5
Language: English
Teacher: Letizia Pellegrini, Alberto Peretti
Microeconomics 1
Credits: 10,5
Language: English
Teacher: Simona Fiore, Claudio Zoli, Martina Menon
Political economy
Credits: 5
Language: English
Teacher: Emanuele Bracco, Roberto Ricciuti
Probability
Credits: 7,5
Language: English
Teacher: Marco Minozzo
Introduction to Probability – Module I (2022/2023)
Teacher
Referent
Credits
2
Language
English
Class attendance
Free Choice
Location
VERONA
Learning objectives
The purpose of the modules is to explain, at an intermediate level, the basis of probability theory and some of its more relevant theoretical features.
The topics will be illustrated and explained through many examples.
Students are expected to acquire the language and the concepts needed to better understand the probabilistic models and the statistical techniques used in their subjects.
Prerequisites and basic notions
There are not particular learning requirements. It is advisable that students have already been introduced (though at an elementary level) to probability and statistics. It is also advisable that students have some confidence with elementary set theory and mathematical calculus.
Program
- Random experiments, events, event trees.
- Algebras and sigma-algebras, axiomatic definition of probability, probability spaces, properties of probability.
- Conditional probability, Bayes theorem, stochastic independence for events.
- Random variables, measurability, cumulative distribution function.
Bibliography
Didactic methods
Lessons will be delivered via Zoom. Recordings will also be available. Presence to the lessons is not mandatory.
The calendar of the lessons is the following:
5 October 2022, 14:00-16:00
6 October 2022, 9:00-12:00
12 October 2022, 14:00-16:00
13 October 2022, 9:00-10:00
Learning assessment procedures
The final assessment will be through a written paper.
PhD school courses/classes - 2022/2023
PhD students
mattia.argentopagani@univr.it
matteo.benedini@univr.it
laura.bonisoli@univr.it
cesare.cottonaro@univr.it
lidingrong.huang@univr.it
juniorparfait.ngalamo@univr.it
alice.trettenero@univr.it
simone.vecchi@univr.it
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Guidelines for PhD students
Below you will find the files that contain the Guidelines for PhD students and rules for the acquisition of ECTS credits (in Italian: "CFU") for the Academic Year 2023/2024.
Documents
| Title | Info File |
|---|---|
Guidelines PhD students
|
pdf, en, 334 KB, 19/04/24 |
|
Linee guida dottorandi
|
pdf, it, 251 KB, 19/04/24 |
|
Percorso formativo
|
pdf, it, 283 KB, 19/04/24 |
|
Training program
|
pdf, en, 358 KB, 19/04/24 |
