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Optimal estimation problems for non-linear non-Gaussian state-space models do not typically admit analytic solutions. Since their introduction in 1993, particle filtering methods have become a very popular class of algorithms to solve these estimation problems numerically in an online manner, i.e. recursively as observations become available, and are now… (More)

The probability hypothesis density (PHD) filter is a first moment approximation to the evolution of a dynamic point process which can be used to approximate the optimal filtering equations of the multiple-object tracking problem. We show that, under reasonable assumptions, a sequential Monte Carlo (SMC) approximation of the PHD filter converges in mean of… (More)

We present novel sequential Monte Carlo (SMC) algorithms for the simulation of two broad classes of rare events which are suitable for the estimation of tail probabilities and probability density functions in the regions of rare events, as well as the simulation of rare system trajectories. These methods have some connection with previously proposed… (More)

Standard methods for maximum likelihood parameter estimation in latent variable models rely on the Expectation-Maximization algorithm and its Monte Carlo variants. Our approach is different and motivated by similar considerations to simulated annealing; that is we build a sequence of artificial distributions whose support concentrates itself on the set of… (More)

This paper introduces a framework for simulating finite dimensional representations of (jump) diffusion sample paths over finite intervals, without discretisation error (exactly), in such a way that the sample path can be restored at any finite collection of time points. Within this framework we extend existing exact algorithms and introduce novel adaptive… (More)

In this paper, we propose an original approach to the solution of Fredholm equations of the second kind. We interpret the standard von Neumann expansion of the solution as an expectation with respect to a probability distribution defined on a union of subspaces of variable dimension. Based on this representation, it is possible to use trans-dimensional… (More)

We present a sequential Monte Carlo (SMC) method for maximum likelihood (ML) parameter estimation in latent variable models. Standard methods rely on gradient algorithms such as the Expectation-Maximization (EM) algorithm and its Monte Carlo variants. Our approach is different and motivated by similar considerations to simulated annealing (SA); that is we… (More)

- Nicolas P. E. Barry, Anaïs Pitto-Barry, Johanna Tran, Simon E. F. Spencer, Adam M. Johansen, Ana M. Sanchez +5 others
- Chemistry of materials : a publication of the…
- 2015

We deposited Os atoms on S- and Se-doped boronic graphenic surfaces by electron bombardment of micelles containing 16e complexes [Os(p-cymene)(1,2-dicarba-closo-dodecarborane-1,2-diselenate/dithiolate)] encapsulated in a triblock copolymer. The surfaces were characterized by energy-dispersive X-ray (EDX) analysis and electron energy loss spectroscopy of… (More)

- Yan Zhou, John A D Aston, Adam M Johansen
- 2013

We develop strategies for Bayesian modelling as well as model comparison, averaging and selection for compartmental models with particular emphasis on those which occur in the analysis of Positron Emission Tomography (PET) data. Both modelling and computational issues are considered. It is shown that an additive normal error structure does not describe… (More)