The dynamic chain event graph

Barclay, Lorna; Collazo, Rodrigo A.; Smith, Jim Q.; Thwaites, Peter; Nicholson, Ann E.

doi:10.1214/15-ejs1068

Cited by 30 publications

(46 citation statements)

References 22 publications

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“…This not only decreases the size of the event tree, but also makes explicit the asymmetric relationships among steps in the process. CEGs have since further been developed in order to model processes with non-finite event trees [27].…”

Section: Context Specific Independencementioning

confidence: 99%

Gated Bayesian networks for algorithmic trading

Bendtsen

Peña

2016

International Journal of Approximate Reasoning

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This paper introduces a new probabilistic graphical model called gated Bayesian network (GBN). This model evolved from the need to represent processes that include several distinct phases. In essence, a GBN is a model that combines several Bayesian networks (BNs) in such a manner that they may be active or inactive during queries to the model. We use objects called gates to combine BNs, and to activate and deactivate them when predefined logical statements are satisfied. In this paper we also present an algorithm for semi-automatic learning of GBNs. We use the algorithm to learn GBNs that output buy and sell decisions for use in algorithmic trading systems. We show how the learnt GBNs can substantially lower risk towards invested capital, while they at the same time generate similar or better rewards, compared to the benchmark investment strategy buy-and-hold. We also explore some differences and similarities between GBNs and other related formalisms.

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Section: Context Specific Independencementioning

confidence: 99%

Gated Bayesian networks for algorithmic trading

Bendtsen

Peña

2016

International Journal of Approximate Reasoning

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“…It is important to note that the Markov model in [7] was for assessing long-term effectiveness.The CEG described in this paper caters to short-term analysis. Dynamic variants of the CEG have been developed in [1,5]. For applications such as the falls intervention, we observe that individuals take varying amounts of time to move from certain states.…”

Section: Discussionmentioning

confidence: 99%

Modelling with Non-stratified Chain Event Graphs

Shenvi

Smith

Eldridge

2019

Springer Proceedings in Mathematics &Amp; Statistics

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Chain Event Graphs (CEGs) are a recent probabilistic graphical modelling tool that have proved successful in modelling scenarios with context-specific independencies. Although the theory underlying CEGs supports appropriate representation of structural zeroes, the literature so far does not provide an adaptation of the vanilla CEG methods for a real-world application presenting structural zeroes also known as the non-stratified CEG class. To illustrate these methods, we present a non-stratified CEG representing a public health intervention designed to reduce the risk and rate of falling in the elderly. We then compare the CEG model to the more conventional Bayesian Network model when applied to this setting.

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“…However, other classes of graphical models can also entertain a distributed analysis. For example chain event graphs (Smith and Anderson, 2008) and their dynamic variant (Barclay et al, 2013) might be able to provide in some domains a better representation of the involved uncertainties and consequently a more focused decision making. We plan to extend our results to additional classes of model in future research.…”

Section: Discussionmentioning

confidence: 99%

“…Line (1) describes the backward induction step over the time index, t, while line (2) does the same over the index of the vertices of the graph, i. Now note that in lines (5)- (7), Panel G i :ũ t,i using equation (4). Each panel has enough information to do this, since line (10) guarantees that the scores are communicated to the panels overseeing father vertices and line (14) denotes the fact that the SB transmitsû t,i to the appropriate panels.…”

Section: B Proof Of Theoremmentioning

confidence: 99%

Bayesian decision support for complex systems with many distributed experts

Leonelli

Smith

2015

Ann Oper Res

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Complex decision support systems often consist of component modules which, encoding the judgements of panels of domain experts, describe a particular sub-domain of the overall system. Ideally these modules need to be pasted together to provide a comprehensive picture of the whole process. The challenge of building such an integrated system is that, whilst the overall qualitative features are common knowledge to all, the explicit forecasts and their associated uncertainties are only expressed individually by each panel, resulting from its own analysis. The structure of the integrated system therefore needs to facilitate the coherent piecing together of these separate evaluations. If such a system is not available there is a serious danger that this might drive decision makers to incoherent and so indefensible policy choices. In this paper we develop a graphically based framework which embeds a set of conditions, consisting of the agreement usually made in practice of certain probability and utility models, that, if satisfied in a given context, are sufficient to ensure the composite system is truly coherent. Furthermore, we develop new message passing algorithms entailing the transmission of expected utility scores between the panels, that enable the uncertainties within each module to be fully accounted for in the evaluation of the available alternatives in these composite systems.

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The dynamic chain event graph

Cited by 30 publications

References 22 publications

Gated Bayesian networks for algorithmic trading

Gated Bayesian networks for algorithmic trading

Modelling with Non-stratified Chain Event Graphs

Bayesian decision support for complex systems with many distributed experts

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