13,237 research outputs found
Boosting Bayesian Parameter Inference of Nonlinear Stochastic Differential Equation Models by Hamiltonian Scale Separation
Parameter inference is a fundamental problem in data-driven modeling. Given
observed data that is believed to be a realization of some parameterized model,
the aim is to find parameter values that are able to explain the observed data.
In many situations, the dominant sources of uncertainty must be included into
the model, for making reliable predictions. This naturally leads to stochastic
models. Stochastic models render parameter inference much harder, as the aim
then is to find a distribution of likely parameter values. In Bayesian
statistics, which is a consistent framework for data-driven learning, this
so-called posterior distribution can be used to make probabilistic predictions.
We propose a novel, exact and very efficient approach for generating posterior
parameter distributions, for stochastic differential equation models calibrated
to measured time-series. The algorithm is inspired by re-interpreting the
posterior distribution as a statistical mechanics partition function of an
object akin to a polymer, where the measurements are mapped on heavier beads
compared to those of the simulated data. To arrive at distribution samples, we
employ a Hamiltonian Monte Carlo approach combined with a multiple time-scale
integration. A separation of time scales naturally arises if either the number
of measurement points or the number of simulation points becomes large.
Furthermore, at least for 1D problems, we can decouple the harmonic modes
between measurement points and solve the fastest part of their dynamics
analytically. Our approach is applicable to a wide range of inference problems
and is highly parallelizable.Comment: 15 pages, 8 figure
Functional adaptivity for digital library services in e-infrastructures: the gCube approach
We consider the problem of e-Infrastructures that wish to reconcile the generality of their services with the bespoke requirements of diverse user communities. We motivate the requirement of functional adaptivity in the context of gCube, a service-based system that integrates Grid and Digital Library technologies to deploy, operate, and monitor Virtual Research Environments deďŹned over infrastructural resources. We argue that adaptivity requires mapping service interfaces onto multiple implementations, truly alternative interpretations of the same functionality. We then analyse two design solutions in which the alternative implementations are, respectively, full-ďŹedged services and local components of a single service. We associate the latter with lower development costs and increased binding ďŹexibility, and outline a strategy to deploy them dynamically as the payload of service plugins. The result is an infrastructure in which services exhibit multiple behaviours, know how to select the most appropriate behaviour, and can seamlessly learn new behaviours
The OMII Software â Demonstrations and Comparisons between two different deployments for Client-Server Distributed Systems
This paper describes the key elements of the OMII software and the scenarios which OMII software can be deployed to achieve distributed computing in the UK e-Science Community, where two different deployments for Client-Server distributed systems are demonstrated. Scenarios and experiments for each deployment have been described, with its advantages and disadvantages compared and analyzed. We conclude that our first deployment is more relevant for system administrators or developers, and the second deployment is more suitable for usersâ perspective which they can send and check job status for hundred job submissions
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