Sequential Monte Carlo with Highly Informative Observations

By Pierre Del Moral, Lawrence M. Murray. 2014

56 citations recorded by the source corpus.

We propose sequential Monte Carlo (SMC) methods for sampling the posterior distribution of state-space models under highly informative observation regimes, a situation in which standard SMC methods can perform poorly. A special case is simulating bridges between given initial and final values. The basic idea is to introduce a schedule of intermediate weighting and resampling times between observation times, which guide particles towards the final state. This can always be done for continuous-time models, and may be done for discrete-time models under sparse observation regimes; our main focus is on continuous-time diffusion processes. The methods are broadly applicable in that they support multivariate models with partial observation, do not require simulation of the backward transition (which is often unavailable), and, where possible, avoid pointwise evaluation of the forward transition. When simulating bridges, the last characteristic cannot be avoided entirely without concessions, and we suggest an $\...

Subfields: Artificial Intelligence, Statistics and Probability, Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Statistics and Probability

In Probabilistic & Bayesian Methods

Citations in the corpus

Black-Box Variational Inference for Stochastic Differential Equations (2018, background): “Previous related work has focused on ODE epidemic models with time-varying parameters following SDEs (Dureau et al., 2013; Del Moral & Murray, 2015).”
Inference Networks for Sequential Monte Carlo in Graphical Models (2016, background): “Furthermore, for some choices of sequences of densities there is no natural prior distribution, or even it may not be available in closed form.”