28 citations · 42 across the 3 of their papers we have counts for
3 papers
MCMC for Bayesian uncertainty quantification from time-series data
Philip Maybank, Patrick Peltzer, Uwe Naumann +1
Many problems in science and engineering require uncertainty quantification that accounts for observed data. For example, in computational neuroscience, Neural Population Models (N…
Marginal sequential Monte Carlo for doubly intractable models
Richard G. Everitt, Dennis Prangle, Philip Maybank +1
Bayesian inference for models that have an intractable partition function is known as a doubly intractable problem, where standard Monte Carlo methods are not applicable. The past…
Fast approximate Bayesian inference for stable differential equation models
Philip Maybank, Ingo Bojak, Richard G. Everitt
Inference for mechanistic models is challenging because of nonlinear interactions between model parameters and a lack of identifiability. Here we focus on a specific class of mecha…