10,381 research outputs found
Stochastic partial differential equation based modelling of large space-time data sets
Increasingly larger data sets of processes in space and time ask for
statistical models and methods that can cope with such data. We show that the
solution of a stochastic advection-diffusion partial differential equation
provides a flexible model class for spatio-temporal processes which is
computationally feasible also for large data sets. The Gaussian process defined
through the stochastic partial differential equation has in general a
nonseparable covariance structure. Furthermore, its parameters can be
physically interpreted as explicitly modeling phenomena such as transport and
diffusion that occur in many natural processes in diverse fields ranging from
environmental sciences to ecology. In order to obtain computationally efficient
statistical algorithms we use spectral methods to solve the stochastic partial
differential equation. This has the advantage that approximation errors do not
accumulate over time, and that in the spectral space the computational cost
grows linearly with the dimension, the total computational costs of Bayesian or
frequentist inference being dominated by the fast Fourier transform. The
proposed model is applied to postprocessing of precipitation forecasts from a
numerical weather prediction model for northern Switzerland. In contrast to the
raw forecasts from the numerical model, the postprocessed forecasts are
calibrated and quantify prediction uncertainty. Moreover, they outperform the
raw forecasts, in the sense that they have a lower mean absolute error
Momentum-Space Approach to Asymptotic Expansion for Stochastic Filtering
This paper develops an asymptotic expansion technique in momentum space for
stochastic filtering. It is shown that Fourier transformation combined with a
polynomial-function approximation of the nonlinear terms gives a closed
recursive system of ordinary differential equations (ODEs) for the relevant
conditional distribution. Thanks to the simplicity of the ODE system, higher
order calculation can be performed easily. Furthermore, solving ODEs
sequentially with small sub-periods with updated initial conditions makes it
possible to implement a substepping method for asymptotic expansion in a
numerically efficient way. This is found to improve the performance
significantly where otherwise the approximation fails badly. The method is
expected to provide a useful tool for more realistic financial modeling with
unobserved parameters, and also for problems involving nonlinear measure-valued
processes.Comment: revised version for publication in Ann Inst Stat Mat
On an application of extended kalman filtering to activated sludge processes: a benchmark study
The growing demand for performance improvements of urban wastewater system operation coupled with the lack of instrumentation in most wastewater treatment plants motivates the need for non-linear observers to be used as virtual sensors for estimation and control of effluent quality. This paper is focused on the development of a general procedure for on-line monitoring of activated sludge processes, using an extended Kalman filter (EKF) approach. The Activated Sludge Model no.1 (ASM1) is selected to describe the biological processes in the reactor. On-line measurements are corrupted by additive white noise and unknown inputs are modelled using fast Fourier transform (FFT) and spectrum analyses. The given procedure aims at reducing the original ASM1 model to an observable and identifiable model, which can be used for joint non-linear state and parameter estimations. Simulation results are presented to demonstrate the effectiveness of the proposed methods and show that on-line monitoring of SND and XND concentrations is achieved when dynamic input data are used tocharacterize the influent wastewater for the model
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