15,017 research outputs found
Modelling biological invasions: individual to population scales at interfaces
Extracting the population level behaviour of biological systems from that of the individual is critical in understanding dynamics across multiple scales and thus has been the subject of numerous investigations. Here, the influence of spatial heterogeneity in such contexts is explored for interfaces with a separation of the length scales characterising the individual and the interface, a situation that can arise in applications involving cellular modelling. As an illustrative example, we consider cell movement between white and grey matter in the brain which may be relevant in considering the invasive dynamics of glioma. We show that while one can safely neglect intrinsic noise, at least when considering glioma cell invasion, profound differences in population behaviours emerge in the presence of interfaces with only subtle alterations in the dynamics at the individual level. Transport driven by local cell sensing generates predictions of cell accumulations along interfaces where cell motility changes. This behaviour is not predicted with the commonly used Fickian diffusion transport model, but can be extracted from preliminary observations of specific cell lines in recent, novel, cryo-imaging. Consequently, these findings suggest a need to consider the impact of individual behaviour, spatial heterogeneity and especially interfaces in experimental and modelling frameworks of cellular dynamics, for instance in the characterisation of glioma cell motility
An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions
Stochastic differential equations (SDEs) or diffusions are continuous-valued
continuous-time stochastic processes widely used in the applied and
mathematical sciences. Simulating paths from these processes is usually an
intractable problem, and typically involves time-discretization approximations.
We propose an exact Markov chain Monte Carlo sampling algorithm that involves
no such time-discretization error. Our sampler is applicable to the problem of
prior simulation from an SDE, posterior simulation conditioned on noisy
observations, as well as parameter inference given noisy observations. Our work
recasts an existing rejection sampling algorithm for a class of diffusions as a
latent variable model, and then derives an auxiliary variable Gibbs sampling
algorithm that targets the associated joint distribution. At a high level, the
resulting algorithm involves two steps: simulating a random grid of times from
an inhomogeneous Poisson process, and updating the SDE trajectory conditioned
on this grid. Our work allows the vast literature of Monte Carlo sampling
algorithms from the Gaussian process literature to be brought to bear to
applications involving diffusions. We study our method on synthetic and real
datasets, where we demonstrate superior performance over competing methods.Comment: 37 pages, 13 figure
Three-dimensional track reconstruction for directional Dark Matter detection
Directional detection of Dark Matter is a promising search strategy. However,
to perform such detection, a given set of parameters has to be retrieved from
the recoiling tracks : direction, sense and position in the detector volume. In
order to optimize the track reconstruction and to fully exploit the data of
forthcoming directional detectors, we present a likelihood method dedicated to
3D track reconstruction. This new analysis method is applied to the MIMAC
detector. It requires a full simulation of track measurements in order to
compare real tracks to simulated ones. We conclude that a good spatial
resolution can be achieved, i.e. sub-mm in the anode plane and cm along the
drift axis. This opens the possibility to perform a fiducialization of
directional detectors. The angular resolution is shown to range between
20 to 80, depending on the recoil energy, which is however
enough to achieve a high significance discovery of Dark Matter. On the
contrary, we show that sense recognition capability of directional detectors
depends strongly on the recoil energy and the drift distance, with small
efficiency values (50%-70%). We suggest not to consider this information either
for exclusion or discovery of Dark Matter for recoils below 100 keV and then to
focus on axial directional data.Comment: 27 pages, 20 figure
Monte Carlo Estimation of the Density of the Sum of Dependent Random Variables
We study an unbiased estimator for the density of a sum of random variables
that are simulated from a computer model. A numerical study on examples with
copula dependence is conducted where the proposed estimator performs favourably
in terms of variance compared to other unbiased estimators. We provide
applications and extensions to the estimation of marginal densities in Bayesian
statistics and to the estimation of the density of sums of random variables
under Gaussian copula dependence
Multilevel Monte Carlo methods for applications in finance
Since Giles introduced the multilevel Monte Carlo path simulation method
[18], there has been rapid development of the technique for a variety of
applications in computational finance. This paper surveys the progress so far,
highlights the key features in achieving a high rate of multilevel variance
convergence, and suggests directions for future research.Comment: arXiv admin note: text overlap with arXiv:1202.6283; and with
arXiv:1106.4730 by other author
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