Blocking is often used to reduce known variability in designed experiments by collecting together
homogeneous experimental units. Methods to find block designs for discrete data have recently
been proposed by, amongst others, Woods and van de Ven (2011), Niaparast and Schwabe
(2013) and Waite and Woods (2015). In Rappold et al. (2019), we found optimal designs under a
marginal modelling approach when the intra-block dependence structure is defined via a copula.
Defining dependence via a copula model has the advantages of providing a flexible dependence
modelling separate to the marginal probability models, and a more interpretable approach to
defining the degree of dependence. As is common, we used a pseudo-Bayesian approach for
improved robustness. The motivating example is a design for aerospace materials testing experi-
ments to assess thermal properties, and in particular probability of failure.
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