We study the forgetting properties of the particle filter when its state...
There is substantial empirical evidence about the success of dynamic
imp...
Counterfactual inference considers a hypothetical intervention in a para...
The performance of the conditional particle filter (CPF) with backward
s...
We consider particle filters with weakly informative observations (or
`p...
We present an R package bssm for Bayesian non-linear/non-Gaussian state ...
Conditional particle filters (CPFs) are powerful smoothing algorithms fo...
We propose a conditional log Gaussian Cox process (LGCP) model to invest...
Approximate Bayesian computation allows for inference of complicated
pro...
Approximate Bayesian computation (ABC) allows for inference of complicat...
Approximate inference in probabilistic graphical models (PGMs) can be gr...
We develop an importance sampling (IS) type estimator for Bayesian joint...
We consider the coupled conditional backward sampling particle filter (C...