Dr Owen Jones

Homepage of Owen Jones

Teaching

Selected Publications

Research interests: stochastic modelling and simulation, in particular with regards to self-similar processes (including branching processes, which are something of a canonical example).

  • Zaeem Burq and Owen Jones, Simulation of Brownian motion at first passage times.
    Mathematics and Computers in Simulation, 77, pp. 64-71, 2008. DOI Preprint version.
  • Davey, A.J.H., Doncaster, C.P. and Jones, O.D., A Stochastic Model for Shelter Use in a Mobile Fish Population: the Effect of Intraspecific Competition.
    In Oxley, L. and Kulasiri, D. (eds), MODSIM 2007 International Conference on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2007, pp. 2889-2895, 2007. On-line version
  • Andre Costa, Owen Jones and Dirk Kroese. Convergence Properties of the Cross-Entropy Method for Discrete Optimization.
    Operations Research Letters, 35, pp. 573-580, 2007. DOI Preprint version.
  • Decrouez, G, Amblard, P-O, Brossier, J-M & Jones, O.D., Galton-Watson fractal signals.
    Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2007. IEEE Signal Processing Society.
  • Owen Jones, Modelling Electricity Power Cuts in the UK.
    ANZIAM J. 47 (EMAC2005), pp. C585-C602, 2007. Full text.
  • Johnnie Johnson, Owen Jones and Leilei Tang, Exploring decision makers' use of price information in a speculative market.
    Management Science, vol. 52, pp. 897-907, 2006. Abstract.
  • Wahib Arroum and Owen Jones, Estimation for time-changed self-similar stochastic processes.
    Proceedings SPIE Vol. 6039, Complex Systems, A. Bender Ed. Article 60390F (Jan. 16, 2006). Abstract.
  • Owen Jones and Yuan Shen, A non-parametric test for self-similarity and stationarity in network traffic.
    In Fractals and Engineering: New trends in theory and applications, J. Levy-Vehel and E. Lutton (Eds), pp 219-234, Springer, 2005. Abstract and preprint version.
  • Russell Cheng and Owen Jones, Analysis of distributions in factorial experiments.
    Statistica Sinica, 14, pp 1085--1103, 2004. Abstract.
  • Owen Jones, Large deviations for supercritical multi-type branching processes.
    J. Appl. Probab, 41, pp 703--720, 2004. Abstract.
  • Owen Jones, Fast, efficient on-line simulation of self-similar processes.
    In Thinking in Patterns: Fractals and Related Phenomena in Nature, M.M. Novak Ed., pp 165-176, World Scientific 2004. Abstract and corrected version.
  • Owen Jones and Yuan Shen, Estimating the Hurst index of a self-similar process via the crossing tree.
    Signal Processing Letters, 11, pp 416--419, 2004. Abstract.
  • Ben Hambly and Owen Jones, Thick and thin points for random recursive fractals.
    Adv. Appl. Prob., 35(2003), 251-277. Abstract.
  • Ben Hambly and Owen Jones, Asymptotically one-dimensional diffusion on the Sierpinski gasket and multi-type branching processes with varying environment.
    J. Theoret. Prob., 15(2002), 285-322. Abstract.
  • Owen Jones, Ted White and Bronwen Butler, Estimating crystal growth rates using computed tomography.
    Inverse Problems, 16(2000), 1477-1485. Abstract.
  • Owen Jones, On the convergence of multi-type branching processes with varying environments.
    Ann. Appl. Prob., 7(1997), 772-801. Full text (postscript) and errata (postscript).
  • Owen Jones, Transition probabilities for the simple random walk on the Sierpinski graph.
    Stoc. Proc. Appl., 61(1996), 45-69. Abstract.

Software

Individuals are free to use this code for the purpose academic research, provided it is properly acknowledged. For any other use, permission must first be arranged with the author.

Matlab and R code for simulating Brownian motion as it first crosses points in delta*Z. A description of the algorithm is given in the paper "Simulation of Brownian motion at first-passage times".

Matlab code for estimating the Hurst index H of a self-similar process. Details of the algorithm are available in the paper "Estimating the Hurst index of a self-similar process via the crossing tree".

Matlab code for simulating EBP self-similar processes. Details of the algorithm are available in the paper "Fast, efficient on-line simulation of self-similar processes".

Matlab code for simulating Multitype-EBP (MEBP) processes. MEBP processes are a generalisation of EBP processes, which incorporate a multifractal time-change defined using a cascade process on the crossing tree.

  • MEBP.m an easy to use front end
  • MEBPsim.m a more flexible front end (saves a process state which allows later simulation of additional points)
  • MEBPincr.m used by MEBPsim
  • MEBPexpd.m used by MEBPsim
  • MEBPinit.m used to generate initial state
If you are using Matlab version 6 (release 12) then the following functions replace MEBP.m

Other Information

Some links to (work related) places I'm fond of: