Dr Owen Jones

Homepage of Owen Jones

Teaching

Book

Introduction to Scientific Programming and Simulation Using R
Owen Jones, Robert Maillardet and Andrew Robinson. Taylor and Francis, 2009.

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).

  • Decrouez, G., Amblard, P-O., Brossier, J-M and Jones, O.D., Galton-Watson iterated function systems.
    J. Phys. A: Math. Theor., 42, pp. 095101-095117, 2009. DOI Preprint version
  • Davey, A.J.H., Doncaster, C.P. and Jones, O.D., Distinguishing between interference and exploitation competition for shelter in a mobile fish population.
    To appear in Environmental Modelling and Assessment. DOI Preprint version and data
  • 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.
  • 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.
  • 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.
  • 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(s).

Simulating Brownian motion at first passage times

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".

Estimation of the Hurst index

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".

Embedded Branching Process (EBP) self-similar processes

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".

Multifractal-EBP processes

Matlab code for simulating Multifractal-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

Other Information

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