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Bayesian nonparametric statistics (currently not offered)
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Bayesian nonparametric statistics (currently not offered)
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Academic year 2019/2020
- Course ID
- MAT0042
- Teaching staff
-
Matteo Ruggiero
- 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
- Formal authority
- 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 asymptotics16 hours of the course will be taught by Visiting Professor Ramses Mena on Random partitions and dependent processes.
Suggested readings and bibliography
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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.- Oggetto:
Class schedule
Days Time Classroom Tuesday 14:00 - 16:00 Aula 09 - Edificio Storico (3° piano) Polo di Management ed Economia Thursday 14:00 - 16:00 Aula 10 - Edificio Storico (3° piano) 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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