Refining a Bayesian network using a chain event graph

The search for a useful explanatory model based on a Bayesian Network (BN) now has a long and successful history. However, when the dependence structure between the variables of the problem is asymmetric then this cannot be captured by the BN. The Chain Event Graph (CEG) provides a richer class of models which incorporates these types of dependence structures as well as retaining the property that conclusions can be easily read back to the client. We demonstrate on a real health study how the CEG leads us to promising higher scoring models and further enables us to make more refined conclusions than can be made from the BN. Further we show how these graphs can express causal hypotheses about possible interventions that could be enforced

Human experience in the natural and built environment : implications for research policy and practice

22nd IAPS conference. Edited book of abstracts. 427 pp. University of Strathclyde, Sheffield and West of Scotland Publication. ISBN: 978-0-94-764988-3

Emerging role of endosomal toll-like receptors in rheumatoid arthritis

Toll-like receptors (TLRs) and their downstream signaling pathways have been comprehensively characterized in innate immunity. In addition to this function, these receptors have also been suggested to be involved in the pathogenesis of many autoimmune diseases, including rheumatoid arthritis (RA). Murine in vivo models and human in vitro tissue models of RA have provided a wealth of information on the potential activity of TLRs and components of the downstream signaling pathways. Whilst most early work investigated the cell surface TLRs, more recently the focus has moved to the endosomal TLRs 3, 7, 8, and 9. These receptors recognize self and foreign double-stranded RNA and single-stranded RNA and DNA. The development of therapeutics to inhibit the endosomal TLRs or components of their signaling cascades may represent a way to target inflammation upstream of cytokine production. This may allow for greater specificity than existing therapies including cytokine blockade. Here, we review the current information suggesting a role for the endosomal TLRs in RA pathogenesis and the efforts to target these receptors therapeutically

Decision making with decision event graphs

We introduce a new modelling representation, the Decision Event Graph (DEG), for asymmetric multistage decision problems. The DEG explicitly encodes conditional independences and has additional significant advantages over other representations of asymmetric decision problems. The colouring of edges makes it possible to identify conditional independences on decision trees, and these coloured trees serve as a basis for the construction of the DEG. We provide an efficient backward-induction algorithm for finding optimal decision rules on DEGs, and work through an example showing the efficacy of these graphs. Simplifications of the topology of a DEG admit analogues to the sufficiency principle and barren node deletion steps used with influence diagrams