We provide a framework which admits a number of “marginal” sequential Mo...
In Bayesian inverse problems, one aims at characterizing the posterior
d...
We propose a divide-and-conquer approach to filtering which decomposes t...
Solving Fredholm equations of the first kind is crucial in many areas of...
Building on (Neal and Hinton, 1998), where the problem tackled by EM is
...
We revisit the divide-and-conquer sequential Monte Carlo (DaC-SMC) algor...
Combining several (sample approximations of) distributions, which we ter...
Interacting particle populations undergoing repeated mutation and
fitnes...
Motivated by problems from neuroimaging in which existing approaches mak...
We introduce a class of Monte Carlo estimators for product-form target
d...
For rare events described in terms of Markov processes, truly unbiased
e...
Fredholm integral equations of the first kind are the prototypical examp...
Sequential Monte Carlo algorithms are popular methods for approximating
...
Large deviations for additive path functionals of stochastic processes h...
For Bayesian inference with large data sets, it is often convenient or
n...
Sequential Monte Carlo (SMC) methods, also known as particle filters,
co...
We consider weighted particle systems of fixed size, in which a new
gene...
Models for which the likelihood function can be evaluated only up to a
p...