Imke Botha
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View article: Adaptive Bayesian Algorithms for Complex State Space and Mathematical Models
Adaptive Bayesian Algorithms for Complex State Space and Mathematical Models Open
Calibrating statistical models to data can be a challenging task, particularly when the model is difficult or time consuming to evaluate. Methods that infer the parameters of these models often require significant practitioner effort and/o…
View article: Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance
Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance Open
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 for parameter inference of static Bayesian models with a G…
View article: Assimilating modelled dissolved inorganic nitrogen loads to monitored data using component-wise iterative ensemble Kalman inversion
Assimilating modelled dissolved inorganic nitrogen loads to monitored data using component-wise iterative ensemble Kalman inversion Open
Understanding the fate of dissolved inorganic nitrogen (DIN) sourced from land-based runoff is an important element in the development of strategies for managing water quality on the Great Barrier Reef (GBR). Direct measurement of the gene…
View article: Adaptively switching between a particle marginal Metropolis-Hastings and a particle Gibbs kernel in SMC$^2$
Adaptively switching between a particle marginal Metropolis-Hastings and a particle Gibbs kernel in SMC$^2$ Open
Sequential Monte Carlo squared (SMC$^2$; Chopin et al., 2012) methods can be used to sample from the exact posterior distribution of intractable likelihood state space models. These methods are the SMC analogue to particle Markov chain Mon…
View article: Automatically adapting the number of state particles in SMC$$^2$$
Automatically adapting the number of state particles in SMC$$^2$$ Open
Sequential Monte Carlo squared (SMC $$^2$$ ) methods can be used for parameter inference of intractable likelihood state-space models. These methods replace the likelihood with an unbiased particle filter estimate, similarly to particle…
View article: Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance
Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance Open
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 for parameter inference of static Bayesian models with a G…
View article: Automatically adapting the number of state particles in SMC$^2$
Automatically adapting the number of state particles in SMC$^2$ Open
Sequential Monte Carlo squared (SMC$^2$) methods can be used for parameter inference of intractable likelihood state-space models. These methods replace the likelihood with an unbiased particle filter estimator, similarly to particle Marko…
View article: Particle Methods for Stochastic Differential Equation Mixed Effects Models
Particle Methods for Stochastic Differential Equation Mixed Effects Models Open
Parameter inference for stochastic differential equation mixed effects models (SDEMEMs) is a challenging problem. Analytical solutions for these models are rarely available, which means that the likelihood is also intractable. In this case…
View article: Bayesian inference for stochastic differential equation mixed effects models
Bayesian inference for stochastic differential equation mixed effects models Open
Stochastic differential equation mixed effects models (SDEMEMs) are increasingly used in biomedical and pharmacokinetic/pharmacodynamic research. However, the complexity of these models means that previous research has focussed on approxim…