5,124 research outputs found
Inverse Problems in a Bayesian Setting
In a Bayesian setting, inverse problems and uncertainty quantification (UQ)
--- the propagation of uncertainty through a computational (forward) model ---
are strongly connected. In the form of conditional expectation the Bayesian
update becomes computationally attractive. We give a detailed account of this
approach via conditional approximation, various approximations, and the
construction of filters. Together with a functional or spectral approach for
the forward UQ there is no need for time-consuming and slowly convergent Monte
Carlo sampling. The developed sampling-free non-linear Bayesian update in form
of a filter is derived from the variational problem associated with conditional
expectation. This formulation in general calls for further discretisation to
make the computation possible, and we choose a polynomial approximation. After
giving details on the actual computation in the framework of functional or
spectral approximations, we demonstrate the workings of the algorithm on a
number of examples of increasing complexity. At last, we compare the linear and
nonlinear Bayesian update in form of a filter on some examples.Comment: arXiv admin note: substantial text overlap with arXiv:1312.504
Parameter Estimation via Conditional Expectation --- A Bayesian Inversion
When a mathematical or computational model is used to analyse some system, it
is usual that some parameters resp.\ functions or fields in the model are not
known, and hence uncertain. These parametric quantities are then identified by
actual observations of the response of the real system. In a probabilistic
setting, Bayes's theory is the proper mathematical background for this
identification process. The possibility of being able to compute a conditional
expectation turns out to be crucial for this purpose. We show how this
theoretical background can be used in an actual numerical procedure, and
shortly discuss various numerical approximations
A wildland fire model with data assimilation
A wildfire model is formulated based on balance equations for energy and
fuel, where the fuel loss due to combustion corresponds to the fuel reaction
rate. The resulting coupled partial differential equations have coefficients
that can be approximated from prior measurements of wildfires. An ensemble
Kalman filter technique with regularization is then used to assimilate
temperatures measured at selected points into running wildfire simulations. The
assimilation technique is able to modify the simulations to track the
measurements correctly even if the simulations were started with an erroneous
ignition location that is quite far away from the correct one.Comment: 35 pages, 12 figures; minor revision January 2008. Original version
available from http://www-math.cudenver.edu/ccm/report
Continuous-Discrete Path Integral Filtering
A summary of the relationship between the Langevin equation,
Fokker-Planck-Kolmogorov forward equation (FPKfe) and the Feynman path integral
descriptions of stochastic processes relevant for the solution of the
continuous-discrete filtering problem is provided in this paper. The practical
utility of the path integral formula is demonstrated via some nontrivial
examples. Specifically, it is shown that the simplest approximation of the path
integral formula for the fundamental solution of the FPKfe can be applied to
solve nonlinear continuous-discrete filtering problems quite accurately. The
Dirac-Feynman path integral filtering algorithm is quite simple, and is
suitable for real-time implementation.Comment: 35 pages, 18 figures, JHEP3 clas
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