Publications (6)
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…
A Bayesian spatio-temporal nearest neighbor Gaussian process model for pooled genetic data
Imke Botha, Tianxiao Hao, Lucinda E. Harrison +3
Large scale genetic datasets often aggregate the total allele counts of distinct genetic markers. Inferring haplotype frequencies (i.e.\ the frequency of multimarker alleles) from…
A nonparametric approach to practical identifiability of nonlinear mixed effects models
Tyler Cassidy, Stuart T. Johnston, Michael Plank +4
Mathematical modelling is a widely used approach to understand and interpret clinical trial data. This modelling typically involves fitting mechanistic mathematical models to data…
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…
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…