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Bayesian nonparametric statistics

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Bayesian nonparametric statistics

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Academic year 2016/2017

Course ID
MAT0042
Teaching staff
Prof. Antonio Canale
Prof. Matteo Ruggiero
Ramses H. Mena
Year
2nd year
Teaching period
First semester
Type
D.M. 270 TAF C - Related or integrative
Credits/Recognition
6
Course disciplinary sector (SSD)
SECS-S/01 - statistica
Delivery
Class Lecture
Language
English
Attendance
Optional
Type of examination
Oral
Prerequisites
STOCHASTIC MODELLING FOR STATISTICAL APPLICATIONS
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Sommario del corso

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Course objectives

The course aims at providing a modern overview of Bayesian nonparametric statistical methods. 

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Results of learning outcomes

Students will learn how to model statistical problems with Bayesian nonparametric tools, study theoretical properties of the involved objects and devise appropriate computational algorithms for their implementation. 

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Course delivery

The course consists mainly of class lectures, with some additional computer lab sessions using R.

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Learning assessment methods

Oral examination and optional paper presentation or discussion of an essay elaborated by the student.

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Program

This course covers the fundamentals of Bayesian nonparametric inference and focuses on the key
probabilistic concepts and stochastic modelling tools at the basis of the most recent advances in the field:
• foundations of Bayesian nonparametric inference: exchangeability and de Finetti's representation
theorem
• the Dirichlet process
• models beyond the Dirichlet process
• mixture models for density estimation and clustering
• random partitions
• dependent priors for partially exchangeable data
• elements of Bayesian asymptotics

16 hours of the course will be taught by Visiting Professor Ramses Mena on Random partitions and dependent processes.

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Suggested textbooks and readings

GHOSAL and VAN DER VAART (2016). Theory of nonparametric Bayesian inference. Cambridge University
Press.
HJORT, HOLMES, MUELLER and WALKER (eds.) (2010). Bayesian Nonparametrics. Cambridge University
Press.
GHOSH, RAMAMOORTHI. (2003). Bayesian Nonparametrics. Springer.

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Class schedule

DaysTimeClassroom
Tuesday14:00 - 16:00Aula 9 - Edificio Storico Polo di Management ed Economia
Thursday14:00 - 16:00Aula 10 - Edificio Storico Polo di Management ed Economia

Lessons: dal 27/09/2016 to 09/12/2016

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Note

This course will be delivered at the ESOMAS Department.

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Last update: 28/11/2016 15:47
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