Academic year 2018/2019
- Course ID
- Teaching staff
- Matteo Ruggiero
- 2nd year
- Teaching period
- First semester
- D.M. 270 TAF C - Related or integrative
- Course disciplinary sector (SSD)
- SECS-S/01 - statistica
- Formal authority
- Type of examination
- STOCHASTIC MODELLING FOR STATISTICAL APPLICATIONS
Sommario del corso
Bayesian statistics constitutes the main alternative approach to classical statistical inference, which is based on frequentist principles. Its approach and advantages have nowadays found widespread appreciation by the most diverse scientific communities, as witnesses by the countless registered applications of Bayesian methods to virtually any applied discipline. The course aims at providing a modern overview of Bayesian statistical methods, covering the fundamentals of both the parametric and the nonparametric approach. The course will focus on the key probabilistic and foundational concepts, stochastic modelling tools and most widely used computational strategies at the basis of the most recent advances in the field.
A short module of the course, included in the overall courseload, will be taught by Visiting Professor Omiros Papaspiliopoulos (Universitat Pompeu Fabra) on Scalable algorithms in modern Bayesian computation (see International visiting professors).
Results of learning outcomes
- Knowledge and understanding
Students will learn how to model statistical problems with Bayesian parametric and nonparametric tools, with a deep insight into their theoretical properties.
- Applying knowledge and understanding
The student will possess the ability to set up a simple Bayesian model to analyse univariate data, and the capacity to devise appropriate computational algorithms for their implementation. The knowledge will be suffienct for reading and understanding a scientific paper on topics coherent with the course contents.
- Making judgements
The student will be able to select the appropriate Bayesian model to fit different types of univariate data. They will be also able to understand when parametric models are to restrictive and a Bayesian Nonparametric approach could be more appropriate.
- Communication skills
The Student will be able to use appropriate, formal statistical language to describe a Bayesian statistical model and its properties, to discuss about Bayesian modelling and when one approach is more appropriate than another, to communicate the results of their findings on model implementations, to summarize and discuss a scientific paper on a coherent topic in an oral presentation.
The course consists of roughly 80% of class lectures, and 20% of computer lab sessions.
Learning assessment methods
The exam consists in the oral verification of the material covered in class on the parametric and the nonparametric module. The final mark will be the average of the two evaluations given by the two lecturers on the respective modules (parametric and nonparametric).
In addition to the above, students can optionally present and discuss a scientific paper (e.g., one of those referenced during the course) with the aid of a slide presentation (in which case they should bring their own personal device to the exam). The students are encouraged to agree upon the paper to be presented with the teacher whose module is more relevant for the topic. In this case, the examination on such module will be shorter. Only one paper to one teacher can be presented.
Bayesian parametric statistics:
- Motivation and foundations of Bayesian inference: exchangeability and de Finetti's representation theorems
- Conjugacy, posteriors and parametric families of conjugate models
- Markov chain Monte Carlo methods for parametric inference
Bayesian nonparametric statistics:
- The Bayesian nonparametric framework
- The Dirichlet process: definition and properties
- Constructions of the Dirichlet process
- Hierarchical priors derived from the Dirichlet process
- Prior models beyond the Dirichlet process
Suggested readings and bibliography
Lecture notes will be made available. Additional suggested reading are:
HOFF, P.D. (2009). A First Course in Bayesian Statistical Methods. Springer.
GHOSAL, S. and VAN DER VAART, A. (2017). Theory of nonparametric Bayesian inference. Cambridge University Press.
HJORT, N., HOLMES, C., MUELLER, P. and WALKER, S.G. (eds.) (2010). Bayesian Nonparametrics. Cambridge University Press.
GHOSH, J.K. and RAMAMOORTHI, R.V. (2003). Bayesian Nonparametrics. Springer.
This course will be delivered at the ESOMAS Department.