21 citations · 71 across the 15 of their papers we have counts for
11 papers · 1 filter
Logistic regression models for aggregated data
Tom Whitaker, Boris Beranger, Scott A. Sisson
Logistic regression models are a popular and effective method to predict the probability of categorical response data. However inference for these models can become computationally…
Scalable Deep Generative Relational Models with High-Order Node Dependence
Xuhui Fan, Bin Li, Scott Anthony Sisson +2
We propose a probabilistic framework for modelling and exploring the latent structure of relational data. Given feature information for the nodes in a network, the scalable deep ge…
Multiclass classification of growth curves using random change points and heterogeneous random effects
Vincent Chin, Jarod Y. L. Lee, Louise M. Ryan +2
Faltering growth among children is a nutritional problem prevalent in low to medium income countries; it is generally defined as a slower rate of growth compared to a reference hea…
Efficient Bayesian synthetic likelihood with whitening transformations
Jacob W. Priddle, Scott A. Sisson, David T. Frazier +1
Likelihood-free methods are an established approach for performing approximate Bayesian inference for models with intractable likelihood functions. However, they can be computation…
Composite likelihood methods for histogram-valued random variables
Thomas Whitaker, Boris Beranger, Scott A. Sisson
Symbolic data analysis has been proposed as a technique for summarising large and complex datasets into a much smaller and tractable number of distributions -- such as random recta…
High-dimensional inference using the extremal skew- process
B. Beranger, A. G. Stephenson, S. A. Sisson
Max-stable processes are a popular tool for the study of environmental extremes, and the extremal skew- process is a general model that allows for a flexible extremal dependence…