Bayesian Experimental Design for Stochastic Kinetic Models Seminar

In recent years, the use of the Bayesian paradigm for estimating the optimal experimental design has increased. However, standard techniques are computationally intensive for even relatively small stochastic kinetic models. One solution to this problem is to use to the linear noise approximation. This approximation provides two benefits. First, parameter inference is fast and efficient. Second, we can use linear noise approximation to propose average realisations, allowing us to quickly reduce the design space. This talk will discuss the strengths and limitations of this approach.