Bayesian computation

A central goal in Bayesian computation is designing MCMC algorithms that apply broadly, need no manual tuning and scale efficiently with dimension. Elliptical slice sampling meets many of these needs: it is tuning-free, does not require a differentiable target, adapts its step size to local geometry automatically and, through generalized elliptical slice sampling, applies to a broad class of target distributions. Under favourable conditions its mixing time can scale logarithmically with dimension, making it more scalable than Hamiltonian Monte Carlo and an optimally tuned adaptive random walk.

Fate decisions in developing tissues involve cells transitioning between a set of discrete cell states. Geometric models, often referred to as Waddington landscapes, are an appealing way to describe differentiation dynamics and developmental decisions. We consider the differentiation of neural and mesodermal cells from pluripotent mouse embryonic stem cells exposed to different combinations and durations of signalling factors. We developed a principled statistical approach using flow cytometry data to quantify differentiating cell states. Then, using a framework based on Catastrophe Theory and