### Teaching language
English
_Obs.: As aulas serão em português caso todos dominem esta língua._
### Objectives
The general objective of this lecture course is to familiarize students with some techniques currently used in data analysis in Astronomy. In particular, it is intended that students develop an understanding of the main concepts underpinning the process of scientific inference and become capable of applying them when trying to solve problems in Astronomy.
### Learning outcomes and competences
It is expected that the student will be able to apply the methods associated with the process of scientific inference to the analysis of data and the resolution of problems in Astronomy.
### Working method
Presencial
### Program
\- Deductive and inductive inference in the scientific method.
\- Parameter estimation and model comparison in Physics and Astronomy: exemplification through the analysis of spectra and detection of sources.
\- Analytical fitting of linear physical models in the presence of Gaussian uncertainties.
\- Computational fitting of nonlinear physical models.
\- Analysis of time series and images.
\- Definition of experimental and observational strategies in Physics and Astronomy.
### Mandatory literature
P. C. Gregory; Bayesian Logical Data Analysis for the Physical Sciences, 2005
W. von der Linden, V. Dose, U. von Toussaint; Bayesian Probability Theory: Applications in the Physical Sciences, 2014
### Complementary Bibliography
S. Andreon, B. Weaver; Bayesian Methods for the Physical Sciences, 2015
Bailer-Jones, C.A.L.; Practical Bayesian Inference: A Primer for Physical Scientists, 2017
J.M. Hilbe, R.S. de Souza and E.E.O. Ishida; Bayesian Models for Astrophysical Data, 2017
### Teaching methods and learning activities
In the theoretical-practical classes, the syllabus is explained and its application exemplified. Problems illustrating the concepts presented are also solved, and discussion is promoted in the classroom, contributing to the consolidation of knowledge and the development of a critical mind. In the practical-laboratorial classes, methods and techniques are implemented that can be used in the context of the analysis of data, such as spectra, time series and images, relevant for Physics and Astronomy.
### Evaluation Type
Distributed evaluation with final exam
### Assessment Components
Exam: 35,00%
Written assignment: 65,00%
**Total:**: 100,00%
### Amount of time allocated to each course unit
Autonomous study: 106,00 hours
Frequency of lectures: 56,00 hours
**Total:**: 162,00 hours
### Eligibility for exams
In the final exam students are required to obtain a minimum classification of 8 in 20.
### Calculation formula of final grade
The final classification is given by: Nf=0.35\*Ex+0.35\*Tr1+0.30\*Tr2 where Nf is the final classification (cannot be below 10 in a scale of 0 to 20), Ex is the classification in the final exam (cannot be below 8 in a scale of 0 to 20), Tr1 and Tr2 are the overall classifications respectively in the first and second pratical work tasks with written report (between 0 and 20).
### Examinations or Special Assignments
Pratical work tasks with required submission of written reports will be given to all students, and their classification will have a weight of 65 per cent towards the final classification.
### Classification improvement
The improvement of the final classification can be made only by improving the classification in the written exam, that will still have a weigh of 35 percent in the final classification. It will not be possible to improve the classification in the pratical work tasks.
More information at: https://sigarra.up.pt/fcup/en/ucurr_geral.ficha_uc_view?pv_ocorrencia_id=498806
