Statistical Signal Processing
Faculteit  Science and Engineering 
Jaar  2022/23 
Vakcode  WBAS00905 
Vaknaam  Statistical Signal Processing 
Niveau(s)  bachelor 
Voertaal  Engels 
Periode  semester I b 
ECTS  5 
Rooster  rooster.rug.nl 
Uitgebreide vaknaam  Statistical Signal Processing  
Leerdoelen  At the end of the course, the student is able to: 1. understand and derive the basic theory of (regularised) linear and nonlinear inference in a Bayesian statistical framework and understand their limits; 2. derive a (non)linear data model for a given inference problem and solve for the model parameter values and their uncertainties; 3. apply statistical estimation methods on data using various numerical methods, coded by the student in python (notebook). 

Omschrijving  The aim of this course is to introduce the students to the basics of Statistical Signal Processing with emphasis on the application of this field to Bayesian data modelling. Such methods play a crucial role in the analysis and interpretation of data in almost every field of science. The course will present the general mathematical and statistical framework of Statistical Signal Processing with special emphasis on examples from Astronomy and Physics. The course will cover among others, the topics of Random vectors and processes, linear and nonlinear estimation theory, Gaussian process regression, MarkovChain Monte Carlo methods. Problem sets and computer assignments are a substantial and integral part of the course.  
Uren per week  
Onderwijsvorm 
Hoorcollege (LC), Practisch werk (PRC), Werkcollege (T)
(32 LC, 32 PRC, 16 T, 60 self study) 

Toetsvorm 
Opdracht (AST), Schriftelijk tentamen (WE), Verslag (R)
(40% WE, 20 % R, 40 % AST) 

Vaksoort  bachelor  
Coördinator  prof. dr. L.V.E. Koopmans  
Docent(en)  prof. dr. L.V.E. Koopmans  
Verplichte literatuur 


Entreevoorwaarden  The course unit assumes prior knowledge acquired from the main courses taught in the first two years in the astronomy/physics/computer science curriculum. Programming skills in Python (notebook) are highly recommended for the mandatory computer assignments and knowledge of concepts in linear algebra (e.g. basic matrix calculus) and Bayesian statistics will be used (or introduced) during this theoretical and applicationoriented course.  
Opmerkingen  he students have to hand in all computer assignments (one can be dropped) + project to take part in the final exam. The lowest grade of the computers assignments (not including the project) is dropped in the average grade. Students not passing the course after the final first exam can do a reexam, but not the computer assignments. Grades of the latter will not pass to the next academic year. he students are expected to be present for ~2/3rd of the lectures and tutorials. Presence during the tutorials is strongly recommended to ensure success in passing the course. This course was registered last year with course code STBASP12 

Opgenomen in 
