Monte Carlo methods

Education facts

Code: 4MA510

Monte Carlo methods turn randomness into a computational tool. When a problem is too complex to be solved exactly, one can instead simulate many “possible worlds” and use the results to say something about what is expected to happen on average. In this course, you learn the probability-theoretic ideas that make these methods reliable, how to reformulate deterministic problems in probabilistic terms, and the core algorithms underlying simulation. You work with techniques for generating random variables from many types of distributions, simulating more complex stochastic models, and making simulations far more efficient through variance reduction (e.g., control variates, importance sampling, stratification, and Latin hypercube sampling). The course also introduces Metropolis–Hastings and Gibbs sampling in order to draw samples from complicated high-dimensional distributions.

Entry requirements

1MA501 Probability Theory and Statistics 7.5 credits or equivalent course in mathematical statistics, and 15 credits in mathematics at G2F level.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Monte Carlo methods

  Linnaeus University (Kalmar Växjö)

  VÄXJÖ

  Start date: 18 January 2027

  End date: 28 March 2027

  Pace of study: 50 %