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Chain event graphs (CEGs) are a recent family of probabilistic graphical models that generalise the popular Bayesian networks (BNs) family. Crucially, unlike BNs, a CEG is able to embed, within its graph and its statistical model, asymmetries exhibit
Externí odkaz:
http://arxiv.org/abs/2211.11366
Autor:
Strong, Peter, Smith, Jim Q.
Several structure-learning algorithms for staged trees, asymmetric extensions of Bayesian networks, have been proposed. However, these either do not scale efficiently as the number of variables considered increases, a priori restrict the set of model
Externí odkaz:
http://arxiv.org/abs/2211.07228
Staged trees are a recently-developed, powerful family of probabilistic graphical models. An equivalence class of staged trees has now been characterised, and two fundamental statistical operators have been defined to traverse the equivalence class o
Externí odkaz:
http://arxiv.org/abs/2206.15322
Autor:
Strong, Peter, Smith, Jim Q
Chain Event Graphs (CEGs) are a widely applicable class of probabilistic graphical model that can represent context-specific independence statements and asymmetric unfoldings of events in an easily interpretable way. Existing model selection literatu
Externí odkaz:
http://arxiv.org/abs/2202.12352
The effect of fungal pre-treatment using Trametes pubescens on the anaerobic digestion ultrafiltration treatment of wine distillery wastewater (WDW) was studied. The downstream biological treatment system, consisting of four individual reactors, was
Agent-Based Models (ABMs) are often used to model migration and are increasingly used to simulate individual migrant decision-making and unfolding events through a sequence of heuristic if-then rules. However, ABMs lack the methods to embed more prin
Externí odkaz:
http://arxiv.org/abs/2111.04368
Decision making in the face of a disaster requires the consideration of several complex factors. In such cases, Bayesian multi-criteria decision analysis provides a framework for decision making. In this paper, we present how to construct a multi-att
Externí odkaz:
http://arxiv.org/abs/2101.04774
Publikováno v:
In Knowledge-Based Systems 15 August 2023 274
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Autor:
Strong, Peter James, Self, Rachel, Allikian, Kathrine, Szewczyk, Edyta, Speight, Robert, O’Hara, Ian, Harrison, Mark D
Publikováno v:
In Current Opinion in Biotechnology August 2022 76