Stochastic Processes

Postgraduate course

Course description

Objectives and Content

The course will consider Markov processes in discrete and continuous time. The theory is illustrated with examples from operation research, biology and economy.

Learning Outcomes

After completed course, the students are expected to be able to:

  • Carry out derivations involving conditional probability distributions and conditional expectations.
  • Define basic concepts from the theory of Markov chains and present proofs for the most important theorems.
  • Compute probabilities of transition between states and return to the initial state after long time intervals in Markov chains.
  • Identify classes of states in Markov chains and characterize the classes.
  • Determine limit probabilities in Markov chains after an infinitely long period.
  • Derive differential equations for time continuous Markov processes with a discrete state space.
  • Solve differential equations for distributions and expectations in time continuous processes and determine corresponding limit distributions.

Semester of Instruction

Recommended Previous Knowledge
40 ECTS in mathematics and statistics, including courses in calculus, linear algebra and basic statistics (MAT112 Calculus II, MAT121 Linear Algebra and STAT110 Basic Course in Statistics)
Compulsory Assignments and Attendance
Forms of Assessment

Written examination: 4 hours

Examination support materials: Non- programmable calculator, according to model listed in faculty regulations.

Examination only in the autumn.

Grading Scale
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.