MCMC-based tracking and identification of leaders in groups

We present a novel framework for identifying and tracking dominant agents in groups. Our proposed approach relies on a causality detection scheme that is capable of ranking agents with respect to their contribution in shaping the system's collective behaviour based exclusively on the agents' observed trajectories. Further, the reasoning paradigm is made robust to multiple emissions and clutter by employing a class of recently introduced Markov chain Monte Carlo-based group tracking methods. Examples are provided that demonstrate the strong potential of the proposed scheme in identifying actual leaders in swarms of interacting agents and moving crowds. © 2011 IEEE.

Post-earthquake behaviour of footings employing densification to mitigate liquefaction

In situ densification is a popular technique to protect shallow foundations from the effects of earthquake-induced liquefaction, current design being based on semiempirical rules. Poor understanding of the mechanisms governing the performance of soil-structure systems during and after earthquakes inhibits the use of narrow densified zones, which could contribute to optimise the use of densification if the increase in post-earthquake settlement is restrained. Therefore this paper investigates the long-term behaviour of a footing built on densified ground and surrounded by liquefiable ground, centrifuge experiments being used to identify the mechanisms occurring in the ground during and after a seismic simulation. The differential excess pore pressure generated in the ground during the shaking and the processes of vertical stress concentration and subsequent redistribution observed under the footing dominate the system behaviour. The results enlighten the complex mechanisms determining the post-earthquake settlement when densification is carried out to mitigate liquefaction effects. The improvement in performance resulting from widening the zone of densification is rationally explained which encourages the development of new design concepts that may enhance the future use of densification as a liquefaction resistance measure. © 2007 Thomas Telford Ltd.

Asymptotic behaviour of approximate Bayesian estimators

Although approximate Bayesian computation (ABC) has become a popular technique for performing parameter estimation when the likelihood functions are analytically intractable there has not as yet been a complete investigation of the theoretical properties of the resulting estimators. In this paper we give a theoretical analysis of the asymptotic properties of ABC based parameter estimators for hidden Markov models and show that ABC based estimators satisfy asymptotically biased versions of the standard results in the statistical literature.