Two theories of current interest and of mathematical and computational substance
concerning knowledge assessment in education are discussed. These are the theory of …

This article surveys the variety of ways in which a directed acyclic graph (DAG) can be used
to represent a problem of probabilistic causality. For each of these ways, we describe the …

Claiming causal inferences in network settings necessitates careful consideration of the
often complex dependency between outcomes for actors. Of particular importance are …

The chain graph model admits both undirected and directed edges in one graph, where
symmetric conditional dependencies are encoded via undirected edges and asymmetric …

This paper introduces an algorithm to construct a bidirectional causal graph using an
augmented graph. The algorithm decomposes the augmented graph, significantly reducing …

Collapsibility refers to the property that, when marginalizing over some variables that are not
of interest from the full model, the resulting marginal model of the remaining variables is …

Causality plays a pivotal role in various fields of study. Based on the framework of causal
graphical models, previous works have proposed identifying whether a variable is a cause …

Green infrastructures have been widely used to manage urban stormwater, especially in
water-stressed regions. They also pose new challenges to urban and watershed water …

Query rewrite systems perform graph substitutions using rewrite rules to generate optimal
SQL query plans. Rewriting logical and physical relational query plans is proven to be an …

Bayesian networks are widely utilised in various fields, offering elegant representations of
factorisations and causal relationships. We use surjective functions to reduce the …