Mixtures of truncated exponentials (MTEs) are a powerful alternative to discretisation when
working with hybrid Bayesian networks. One of the features of the MTE model is that …

When a Bayesian network (BN) is modified, for example adding or deleting a node, or
changing the probability distributions, we usually will need a total recompilation of the …

There is a strong relationship among cultural landscapes, socio-economy and the provision
of ecosystem services. The goal of this paper is to study the relationships between …

Improving the performance of belief updating becomes increasingly important as real-world
Bayesian networks continue to grow larger and more complex. In this paper, an investigation …

In semiarid areas, precipitations usually appear in the form of big and brief floods, which
affect the aquifer through water infiltration, causing groundwater temperature changes …

Time series analysis requires powerful and robust tools; at the same time the tools must be
intuitive for users. Bayesian networks have been widely applied in static problem modelling …

Qualitative probabilistic networks are designed for probabilistic inference in a qualitative
way. They capture qualitative influences between variables, but do not provide for indicating …

In this paper we introduce a new dynamic importance sampling propagation algorithm for
Bayesian networks. Importance sampling is based on using an auxiliary sampling …

This paper describes some ideas for applying numerical trees in order to represent and
solve asymmetric decision problems with influence diagrams (IDs). Constraint rules are …

Variable elimination (VE) and join tree propagation (JTP) are two alternatives to inference in
Bayesian networks (BNs). VE, which can be viewed as one-way propagation in a join tree …