38,668 research outputs found
Gaussian process approximations of stochastic differential equations
Stochastic differential equations arise naturally in a range of contexts, from financial to environmental modeling. Current solution methods are limited in their representation of the posterior process in the presence of data. In this work, we present a novel Gaussian process approximation to the posterior measure over paths for a general class of stochastic differential equations in the presence of observations. The method is applied to two simple problems: the Ornstein-Uhlenbeck process, of which the exact solution is known and can be compared to, and the double-well system, for which standard approaches such as the ensemble Kalman smoother fail to provide a satisfactory result. Experiments show that our variational approximation is viable and that the results are very promising as the variational approximate solution outperforms standard Gaussian process regression for non-Gaussian Markov processes
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
Marcus versus Stratonovich for Systems with Jump Noise
The famous It\^o-Stratonovich dilemma arises when one examines a dynamical
system with a multiplicative white noise. In physics literature, this dilemma
is often resolved in favour of the Stratonovich prescription because of its two
characteristic properties valid for systems driven by Brownian motion: (i) it
allows physicists to treat stochastic integrals in the same way as conventional
integrals, and (ii) it appears naturally as a result of a small correlation
time limit procedure. On the other hand, the Marcus prescription [IEEE Trans.
Inform. Theory 24, 164 (1978); Stochastics 4, 223 (1981)] should be used to
retain (i) and (ii) for systems driven by a Poisson process, L\'evy flights or
more general jump processes. In present communication we present an in-depth
comparison of the It\^o, Stratonovich, and Marcus equations for systems with
multiplicative jump noise. By the examples of areal-valued linear system and a
complex oscillator with noisy frequency (the Kubo-Anderson oscillator) we
compare solutions obtained with the three prescriptions.Comment: 14 pages, 4 figure
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