  Complex stochastic models represented by directed acyclic graphs (DAGs) are
increasingly employed to synthesise multiple, imperfect and disparate sources
of evidence, to estimate quantities that are difficult to measure directly. The
various data sources are dependent on shared parameters and hence have the
potential to conflict with each other, as well as with the model. In a Bayesian
framework, the model consists of three components: the prior distribution, the
assumed form of the likelihood and structural assumptions. Any of these
components may be incompatible with the observed data. The detection and
quantification of such conflict and of data sources that are inconsistent with
each other is therefore a crucial component of the model criticism process. We
first review Bayesian model criticism, with a focus on conflict detection,
before describing a general diagnostic for detecting and quantifying conflict
between the evidence in different partitions of a DAG. The diagnostic is a
p-value based on splitting the information contributing to inference about a
"separator" node or group of nodes into two independent groups and testing
whether the two groups result in the same inference about the separator
node(s). We illustrate the method with three comprehensive examples: an
evidence synthesis to estimate HIV prevalence; an evidence synthesis to
estimate influenza case-severity; and a hierarchical growth model for rat
weights.
