32 citations · 182 across the 41 of their papers we have counts for
14 papers · 1 filter
Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty
Chatchuea Kimchaiwong, Jeremie Houssineau, Adam M. Johansen
The problem of incorporating information from observations received serially in time is widespread in the field of uncertainty quantification. Within a probabilistic framework, suc…
Cost free hyper-parameter selection/averaging for Bayesian inverse problems with vanilla and Rao-Blackwellized SMC Samplers
Alessandro Viani, Adam M Johansen, Alberto Sorrentino
In Bayesian inverse problems, one aims at characterizing the posterior distribution of a set of unknowns, given indirect measurements. For non-linear/non-Gaussian problems, analyti…
A divide and conquer sequential Monte Carlo approach to high dimensional filtering
Francesca R. Crucinio, Adam M. Johansen
We propose a divide-and-conquer approach to filtering which decomposes the state variable into low-dimensional components to which standard particle filtering tools can be successf…
The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems
Juan Kuntz, Francesca R. Crucinio, Adam M. Johansen
We provide a comprehensive characterisation of the theoretical properties of the divide-and-conquer sequential Monte Carlo (DaC-SMC) algorithm. We firmly establish it as a well-fou…
Divide-and-Conquer Fusion
Ryan S. Y. Chan, Murray Pollock, Adam M. Johansen +1
Combining several (sample approximations of) distributions, which we term sub-posteriors, into a single distribution proportional to their product, is a common challenge. Occurring…
The Node-wise Pseudo-marginal Method
Denishrouf Thesingarajah, Adam M. Johansen
Motivated by problems from neuroimaging in which existing approaches make use of "mass univariate" analysis which neglects spatial structure entirely, but the full joint modelling…