6 citations · 9 across the 2 of their papers we have counts for
5 papers
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…
gk: An R Package for the g-and-k and generalised g-and-h Distributions
Dennis Prangle
The g-and-k and (generalised) g-and-h distributions are flexible univariate distributions which can model highly skewed or heavy tailed data through only four parameters: location…
An ABC interpretation of the multiple auxiliary variable method
Dennis Prangle, Richard G. Everitt
We show that the auxiliary variable method (Møller et al., 2006; Murray et al., 2006) for inference of Markov random fields can be viewed as an approximate Bayesian computation met…
Semi-automatic selection of summary statistics for ABC model choice
Dennis Prangle, Paul Fearnhead, Murray P. Cox +2
A central statistical goal is to choose between alternative explanatory models of data. In many modern applications, such as population genetics, it is not possible to apply standa…
Diagnostic tools of approximate Bayesian computation using the coverage property
D. Prangle, M. G. B. Blum, G. Popovic +1
Approximate Bayesian computation (ABC) is an approach for sampling from an approximate posterior distribution in the presence of a computationally intractable likelihood function.…