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Inferring Leadership from Group Dynamics Using Markov Chain Monte Carlo Methods.

, , , , , and . Modeling, Simulation and Visual Analysis of Crowds, volume 11 of The International Series in Video Computing, Springer, (2013)

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The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking., , and . FUSION, page 1179-1186. IEEE, (2009)Estimating a CBRN atmospheric release in a complex environment using Gaussian processes., , and . FUSION, page 1846-1853. IEEE, (2012)State and impulsive time-varying measurement noise density estimation in nonlinear dynamic systems using Dirichlet Process Mixtures., , , and . ICASSP, page 330-334. IEEE, (2014)Particle filtering with a soft detection based near-optimal importance function for visual tracking., , , , and . EUSIPCO, page 594-598. IEEE, (2015)Location of Things: Geospatial Tagging for IoT Using Time-of-Arrival., , , , and . IEEE Trans. Signal and Information Processing over Networks, 2 (2): 174-185 (2016)Langevin and Hamiltonian Based Sequential MCMC for Efficient Bayesian Filtering in High-Dimensional Spaces., and . J. Sel. Topics Signal Processing, 10 (2): 312-327 (2016)Communications meets copula modeling: Non-standard dependence features in wireless fading channels., , , , and . GlobalSIP, page 1224-1228. IEEE, (2014)Bayesian filtering with intractable likelihood using sequential MCMC., , and . ICASSP, page 6313-6317. IEEE, (2013)Wind storm estimation using a heterogeneous sensor network with high and low resolution sensors., , , and . ICC, page 4865-4870. IEEE, (2015)Skew-t copula for dependence modelling of impulsive (α-stable) interference., , , , , and . ICC, page 4816-4821. IEEE, (2015)