Computational systems biology is concerned with the development of detailed mechanistic
models of biological processes. Such models are often stochastic and analytically intractable, …

Diffusion processes governed by stochastic differential equations (SDEs) are a well-established
tool for modelling continuous time data from a wide range of areas. Consequently, …

In this paper, we adapt recently developed simulation-based sequential algorithms to the
problem concerning the Bayesian analysis of discretely observed diffusion processes. The …

This article is concerned with the Bayesian estimation of stochastic rate constants in the
context of dynamic models of intracellular processes. The underlying discrete stochastic kinetic …

As postgenomic biology becomes more predictive, the ability to infer rate parameters of genetic
and biochemical networks will become increasingly important. In this paper, we explore …

Recently-proposed particle MCMC methods provide a flexible way of performing Bayesian
inference for parameters governing stochastic kinetic models defined as Markov (jump) …

Parameter inference for stochastic differential equations is challenging due to the presence
of a latent diffusion process. Working with an Euler-Maruyama discretisation for the diffusion, …

When conducting Bayesian inference, delayed-acceptance (DA) Metropolis–Hastings (MH)
algorithms and DA pseudo-marginal MH algorithms can be applied when it is …

In South Africa, geography education students’ approach to deep learning has received little
attention. Therefore the purpose of this one-shot experimental case study was to evaluate …

In problem-based learning (PBL), collaboration and productive interactions among group
members are not spontaneous processes. One way to support students in PBL is to integrate …