Chain Event Graphs and Their Semantics
The causal manipulation of chain event graphs
... requires { X 5 = 1 } are not expressed in the diagram. Moreover we might like to incorporate into the representation context specific information because it is informative about various causal hypotheses (see e.g. [7]). ...
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The causal manipulation and Bayesian estimation of chain event graphs
... the semantics of the BN or the factorisation of the probability mass function of the path events, whether the allocation of the prospective second year student occurs before the allocation of the prospective first ...
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Causal discovery through MAP selection of stratified chain event graphs
... The leap from a model search like the one above to causal hypotheses is most commonly addressed through making further hypotheses that the best scoring model retains its structure if a controlled intervention takes ...
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Propagation using chain event graphs
... event tree itself expresses no onditional independenies in its topology and these independenies are the building bloks of urrent propagation algorithms. However, unlike the event tree, the CEG expresses a ...
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Chain event graphs : theory and application
... Also, a pair of positions w3, wt in our CEG are connected by an undirected edge whenever a pair of corresponding vertices va, vj are connected by an undirected edge i[r] ...
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Chain event graphs for informed missingness
... Chain Event Graphs for Informed Missingness Lorna ...Abstract. Chain Event Graphs (CEGs) are proving to be a useful framework for modelling discrete processes which exhibit ...
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Separation theorems for chain event graphs
... spei event, perhaps a spei value of a variable, and use the CEG's topology to disover onditional independenies whih would not exist if we were to ondition on a related event, suh as a dierent v alue of our ...
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Causal analysis with chain event graphs
... original event tree. The CEG retains those advantages that event trees have over BNs for the representation of asymmetri problems; but they are also muh more exible and useful ...
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Learning and predicting with chain event graphs
... that chain event graphs are not just an efficient way of storing the information contained in an event tree, but also a natural way to rep- resent the information that is most easily elicited ...
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Bayesian representations using chain event graphs
... Abstract Bayesian networks (BNs) are useful for coding conditional inde- pendence statements between a given set of measurement variables. On the other hand, event trees (ETs) are convenient for represent- ing ...
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Causal identifiability via chain event graphs
... In this setion we give a brief denition of a CEG. This has been modied slightly sine [16℄ and [22℄. We also provide some notation that will be used throughout the paper. W e then turn our attention to what it means when ...
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Modelling and reasoning with chain event graphs in health studies
... using Chain Event Graphs In this Chapter I will discuss how the CEG provides a new way of systematically exploring the e↵ect of missing covariate data within a study and hence enables us to draw ...
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Using chain event graphs to refine model selection
... Chain Event Graphs (CEGs) are speially designed to embody the onditional indepen- dene struture of problems whose state spaes are asymmetri and do not admit a natural produt ...
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Bayesian MAP model selection of chain event graphs
... P R Figure 5: The MAP CEG for that event tree in Figure 5.1 5.2. Student test data In our second example we apply the learning algorithm to a real dataset in order to test the algorithm’s efficacy in a real-life ...
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Bayesian MAP model selection of chain event graphs
... Another important model class is that which arises from uncertainty about the underlying event tree. A model search algorithm similar to the one described in this paper is possible in this case after setting a ...
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Adaptation of Chain Event Graphs for use with Case-Control Studies in Epidemiology
... 3.2.1 Recruitment by data collection method The effectiveness of different recruitment techniques or data collection strategies (such as web surveys, postal surveys, and electronic reminders) may be of interest. Rather ...
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Covert distributivity in algebraic event semantics
... that event predicates can combine with other event predicates by a generalized form of intersection, similarly to the predicate modification rule in ...
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Overt distributivity in algebraic event semantics
... semantic, atomic form as defined by Link 1987 or in its pragmatic, salience- related form as defined by Schwarzschild 1996 . As we will see, these various uses of each and these silent operators share some part of their ...
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Covert Distributivity in Algebraic Event Semantics
... by the context via pragmatic means? A unified semantic analysis of distributivity should make it apparent which aspects of the meanings of various distributivity operators are always the same, and along which dimensions ...
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Semantics-based Summarization of Entities in Knowledge Graphs
... 7.1. SINGLE ENTITY SUMMARIZATION 118 One limitation that FACES has is that it can successfully group features belonging to object properties as they have types assigned to their values that FACES uses in creating facets. ...
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