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Applied Probability

Real-world applications, such as queueing systems, communication networks and financial markets, evolve in a random fashion over time.

Research in applied probability provides insight into these processes that exhibit randomness using results from the theory of probability, so that they can be mathematically modelled and better understood.

ants

These processes can model real world systems that evolve in a random fashion over time, for example:

  • - queuing systems
  • - communication networks
  • - financial markets

Advances in stochastic approximation, non-Markovian random walks, stochastic control and such techniques have led to developments in other topics, including Monte Carlo Computation and Optimisation under Uncertainty.

Ongoing projects

  • Study on the efficiency of adaptive Markov chain Monte Carlo algorithms (Christophe Andrieu, with Y F Atchade, University of Ottawa)
  • A note on an algorithm proposed by M Beaumont (Christophe Andrieu, with G O Roberts, University of Lancaster)
Recent publications
  • A probabilistic model for the 5k+1 problem and related maps (2006)
    Stanislav Volkov
    Stochastic Processes and their Applications,, vol: 116, Pages: 662 - 674
    URL provided by the author
  • A Central Limit Theorem for non-overlapping return times (2006)
    Oliver Johnson
    Journal of Applied Probability, vol: 43, Issue: 1, Pages: 32 - 47
    DOI: 10.1239/jap/1143936241
    URL provided by the author