Entry requirements

120 credits in Science and Technology including Probability and Statistics

Learning outcomes

On completion of the course, the student should be able to:

• perform calculations with expectations and covariances in stationary processes;
• calculate relationship between covariance properties in the time domain and spectral properties in the frequency domain;
• carry out calculations with linear filters (AR and MA);
• perform simple inference for stationary processes for example estimation of spectrum, filtering;
• suggest appropriate stochastic models of processes that appear in technical applications and carry out prediction.

Content

stationary processes (introduction, definitions); the spectral description; normal processes; filtering (linear systems, filter and cross spectrum); inference for stationary processes; applications (filtering; prediction). advanced case studies where the course content is applied on problems from technology or natural sciences.

Instruction

Lectures, problem-solving sessions, possibly laboratory sessions. Guest lecture.

Assessment

Written examination at the end of the course combined with written assignments during the course, one of which in project form. Instructions are delivered at the start of the course.