3 citations · 6 across the 6 of their papers we have counts for
4 papers · 1 filter
Adaptively switching between a particle marginal Metropolis-Hastings and a particle Gibbs kernel in SMC
Imke Botha, Robert Kohn, Leah South +1
Sequential Monte Carlo squared (SMC; Chopin et al., 2012) methods can be used to sample from the exact posterior distribution of intractable likelihood state space models. Thes…
Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance
Imke Botha, Matthew P. Adams, Dang Khuong Tran +2
The ensemble Kalman filter (EnKF) is a Monte Carlo approximation of the Kalman filter for high dimensional linear Gaussian state space models. EnKF methods have also been developed…
Automatically adapting the number of state particles in SMC
Imke Botha, Robert Kohn, Leah South +1
Sequential Monte Carlo squared (SMC) methods can be used for parameter inference of intractable likelihood state-space models. These methods replace the likelihood with an unbi…
Particle Methods for Stochastic Differential Equation Mixed Effects Models
Imke Botha, Robert Kohn, Christopher Drovandi
Parameter inference for stochastic differential equation mixed effects models (SDEMEMs) is a challenging problem. Analytical solutions for these models are rarely available, which…