Main field(s) of study and in-depth level:
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
G1N: has only upper-secondary level entry requirements
G1F: has less than 60 credits in first-cycle course/s as entry requirements
G1E: contains specially designed degree project for Higher Education Diploma
G2F: has at least 60 credits in first-cycle course/s as entry requirements
G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
GXX: in-depth level of the course cannot be classified.
A1N: has only first-cycle course/s as entry requirements
A1F: has second-cycle course/s as entry requirements
A1E: contains degree project for Master of Arts/Master of Science (60 credits)
A2E: contains degree project for Master of Arts/Master of Science (120 credits)
AXX: in-depth level of the course cannot be classified.
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
The Faculty Board of Science and Technology
120 credits with Probability and Statistics. English language proficiency that corresponds to English studies at upper secondary (high school) level in Sweden ("English 6").
On completion of the course, the student should be able to:
have a general knowledge of the theory of stochastic processes, in particular Markov processes, and be prepared to use Markov processes in various areas of applications;
be familiar with Markov chains in discrete and continuous time with respect to state diagram, recurrence and transience, classification of states, periodicity, irreducibility, etc., and be able to calculate transition probabilities and intensities;
be able to give an account of existence and uniqueness for stationary and asymptotic distributions of Markov chains and, whenever applicable, compute such distributions as solutions of a balance equation;
be able to calculate absorption probabilities and expected absorption time for Markov chains using the principle of conditioning with respect to the first jump;
be able to choose a suitable Markov model in various cases and make suitable calculations, in particular modelling of birth-death processes;
have a knowledge of Markov processes with a continuous state space, in particular a preparatory knowledge of Brownian motion and diffusion, and some understanding of the connection between the theory of Markov processes and differential equations;
have a knowledge of some general Markov method, e.g. Markov Chain Monte Carlo.
The Markov property. Chapman-Kolmogorov's relation, classification of Markov processes, transition probability. Transition intensity, forward and backward equations. Stationary and asymptotic distribution. Convergence of Markov chains. Birth-death processes. Absorption probabilities, absorption time. Brownian motion and diffusion. Geometric Brownian motion. Generalised Markov models. Applications of Markov chains.
Lectures and problem solving sessions.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.