Computational Statistics

Code:

M4142

Acronym:

M4142

Level:

400

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2024/2025 - 1S

Active?	Yes
Responsible unit:	Department of Mathematics
Course/CS Responsible:	Master in Computational Statistics and Data Analysis

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:ECAD	17	Study plan since 2021/2022.	1	-	9	63	243
M:ENM	1	Official Study Plan since 2023/2024	1	-	9	63	243
			2

Teaching language

Suitable for English-speaking students

Objectives

Domain of the most relevant computational methods and principles underlying modern statistical  analysis and inference and application to the analysis of several types of data.

Learning outcomes and competences

Upon completing this course, the student should:

- have a practical understanding of several computational methods, in particular know how these tools may be used in the statistical analysis of different types of data;

-  have a theoretical knowledge of the most relevant computational methods, such as Monte Carlo methods, enabling its use in the development of statistical methods and inference models;

- be able to implement computational tools by means of adequate software and languages, such as R.

Working method

Presencial

Program

Introduction to the statistical programming language R.

Visualisation of multivariate data.

Monte Carlo methods in statistical inference.

Computational inference. 

Bootstrap and Jackknife methods.

Probability density estimation.

Numerical methods in R, including classical estimation methods and algorithms. Maximum likelihood and Expectation-Maximization algorithm.

 

Mandatory literature

Maria L. Rizzo; Statistical computing with R. ISBN: 978-1-4665-5332-3
Gentle, J.E; Elements of Computational Statistics, Springer , 2002
Christian P. Robert; Introducing monte carlo methods with R. ISBN: 978-14419-1575-7

Teaching methods and learning activities

Lectures TP where the topics of the syllabus are presented, exercises and related problems are solved. Classes are accompanied by material provided by teachers.

Two project works to be developed in team. The discussion of the works is open, all students are encouraged to participate.

 

Software

R project

keywords

Physical sciences > Mathematics > Computational mathematics > Computational models
Physical sciences > Mathematics > Statistics

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	33,30
Trabalho escrito	66,70
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	156,00
Frequência das aulas	63,00
Trabalho escrito	24,00
Total:	243,00

Eligibility for exams

Practical assignments submitted within the fixed schedules. Marks for each evaluation component greater than or equal to 6.0 points (0-20 points).

Calculation formula of final grade

The assessment comprises two projects and an exam. The final mark corresponds to the average of the marks obtained in the exam and in the projects. A minimum rating of 30% is required in each of the evaluation components.

Classification improvement

The classification obtained in the works component is not subject to improvement and is only valid during the current academic year.

Observations

Juri: Margarida Brito and Rita Gaio