Jim Q. Smith

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In this paper we investigate the geometry of the likelihood of the unknown parameters in a simple class of Bayesian directed graphs with hidden variables. This enables us, before any numerical algorithms are employed, to obtain certain insights in the nature of the uniden­ tifiability inherent in such models, the way posterior densities will be sensitive to(More)
BACKGROUND Picoeukaryotes represent an important, yet poorly characterized component of marine phytoplankton. The recent genome availability for two species of Ostreococcus and Micromonas has led to the emergence of picophytoplankton comparative genomics. Sequencing has revealed many unexpected features about genome structure and led to several hypotheses(More)
The class of chain event graph models is a generalisation of the class of discrete Bayesian networks, retaining most of the structural advantages of the Bayesian network for model interrogation, propagation and learning, while more naturally encoding asymmetric state spaces and the order in which events happen. In this paper we demonstrate how with complete(More)
a r t i c l e i n f o a b s t r a c t 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(More)
The existing methods of analysis applicable to time budget data are summarised. Latent budget models, a subclass of general reduced rank models for two-way contingency tables, are most appropriate as they view each of the observed conditional distributions of interest as a mixture of a small number of conditional distributions involving a hidden variable.(More)