Conditional path sampling for stochastic differential equations through drift relaxation

Abstract

We present an algorithm for the efficient sampling of conditional paths of stochastic differential equations (SDEs). While unconditional path sampling of SDEs is straightforward, albeit expensive for high dimensional systems of SDEs, conditional path sampling can be difficult even for low dimensional systems. This is because we need to produce sample paths of the SDE that respect both the dynamics of the SDE and the initial and endpoint conditions. The dynamics of a SDE are governed by the deterministic term (drift) and the stochastic term (noise). Instead of producing conditional paths directly from the original SDE, one can consider a sequence of SDEs with modified drifts. The modified drifts should be chosen so that it is easier to produce sample paths that satisfy the initial and endpoint conditions. Also, the sequence of modified drifts should converge to the drift of the original SDE. We construct a simple Markov chain Monte Carlo algorithm that samples, in sequence, conditional paths from the modified SDEs, by taking the last sampled path at each level of the sequence as an initial condition for the sampling at the next level in the sequence. The algorithm can be thought of as a stochastic analog of deterministic homotopy methods for solving nonlinear algebraic equations or as a SDE generalization of simulated annealing. The algorithm is particularly suited for filtering/smoothing applications. We show how it can be used to improve the performance of particle filters. Numerical results for filtering of a stochastic differential equation are included.