Validation of a bayesian belief network representation for posterior probability calculations on national crime victimization survey

Riesen, Michael; Serpen, Gürsel

doi:10.1007/s10506-008-9064-6

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…They have been used in many applications including health, ecology and forensic science to better understand and model complex issues (Kuikka, Varis 1997;Taroni et al 2004;Kjaerulff, Madsen 2012;Riesen, Serpen 2008;Johnson et al 2010). A BN represent variables as nodes and arcs as the direct dependencies between variables (Pearl 1986).…”

Section: Bayesian Networkmentioning

confidence: 99%

Investigating Effective Wayfinding in Airports: A Bayesian Network Approach

et al. 2014

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Effective wayfinding is the successful interplay of human and environmental factors resulting in a person successfully moving from their current position to a desired location in a timely manner. To date this process has not been modelled to reflect this interplay. This paper proposes a complex modelling system approach of wayfinding by using Bayesian Networks to model this process, and applies the model to airports. The model suggests that human factors have a greater impact on effective wayfinding in airports than environmental factors. The greatest influences on human factors are found to be the level of spatial anxiety experienced by travellers and their cognitive and spatial skills. The model also predicted that the navigation pathway that a traveller must traverse has a larger impact on the effectiveness of an airport's environment in promoting effective wayfinding than the terminal design.

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Section: Bayesian Networkmentioning

confidence: 99%

Investigating Effective Wayfinding in Airports: A Bayesian Network Approach

et al. 2014

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“…The proposed modeling approach is also a contribution to the literature on Bayesian network modeling in law because it is one of very few addressing civil litigation. Almost all Bayesian network papers in law have focused on criminal law, with special emphasis on assessing evidence [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52]. A major methodological difference is that much of the modeling in criminal cases is focused on computing likelihood ratios for competing theories of a legal case [53], while the method proposed here focuses on computing the probability of failing to meet a generally accepted standard.…”

Section: Discussionmentioning

confidence: 99%

Hybrid Bayesian Network Models of Spinal Injury and Slip/Fall Events

Hughes¹

2020

Applied Sciences

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Background: Biomechanists are often asked to provide expert opinions in legal proceedings, especially personal injury cases. This often involves using deterministic analysis methods, although the expert is expected to opine using a civil standard of “more likely than not” that is inherently probabilistic. Methods: A method is proposed for converting a class of deterministic biomechanical models into hybrid Bayesian networks that produce a probability well suited for addressing the civil standard of proof. The method was developed for spinal injury during lifting. Its generalizability was assessed by applying it to slip and fall events based on the coefficients of friction at the shoe–floor interface. Results: The proposed method is shown to be generalizable beyond lifting by applying it to a slip and fall event. Both the lifting and slip and fall models showed that incorporating evidence of injury could change the probabilities of critical quantities exceeding a threshold from “less likely than not” to “more likely than not.” Conclusions: The present work shows that it is possible to develop Bayesian networks for legal use based on laws of engineering mechanics and probabilistic descriptions of measurement error and human variability.

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“…Our modelling approach used BBNS to capture the mechanistic understanding of the system elicited from experts. While BBNs are increasingly being used in predictive modelling, only a few are validated (Marcot et al, 2006;Riesen & Serpen, 2008;Stojadinovic et al, 2010) and rarely using independent data (Smith et al, in press;Howes & Maron, 2008). Uncertainty in model structure is also rarely explored in BBNs (Kuhnert & Hayes, 2009).…”

Section: Discussionmentioning

confidence: 99%

Predicting the potential distribution of a riparian invasive plant: the effects of changing climate, flood regimes and land‐use patterns

Murray

Stokes

Klinken

2012

Global Change Biology

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Climate change is likely to affect plants in multiple ways, but predicting the consequences for habitat suitability requires a process-based understanding of the interactions. This is at odds with existing approaches that are mostly phenomenological and largely restricted to predicting the effects of changing temperature and rainfall on species distributions at a coarse spatial scale. We examine the multiple effects of climate change, including predicting the effects of altered flood regimes and land-use change, on the potential distribution of the invasive riparian species lippia (Phyla canescens) across a 26 000 km 2 catchment in eastern Australia. We determined habitat suitability for lippia by combining process-understanding of experts and an eco-physiological bioclimatic model within a Bayesian belief network. The bioclimatic model predicted substantial changes in habitat suitability by 2070 under both a wetter (Echam Mark 3) and drier (Hadley Centre Mark 2) climate change scenario, but only the more likely drier scenario reduced suitability in our test region. The area suitable for lippia was predicted to increase at least threefold with increased flooding under a wet climate scenario, although this would be partially negated by land-use change to cultivation. The region would become unsuitable to lippia with reduced flooding under a drier scenario irrespective of land-use changes, although existing populations would persist if grazing persisted. Independent field validation verified model structure and parameterization, and therefore the opinion of experts, but identified site-scale deficiencies in the available environmental data layers. Model predictions suggest that adaptation options for managing lippia will be greatly reduced under a drying scenario, but identify potential restoration opportunities under either scenario. This work highlights the value of predictive models that incorporate process-understanding at sufficiently fine spatial resolution to capture the important processes underpinning habitat suitability.

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Validation of a bayesian belief network representation for posterior probability calculations on national crime victimization survey

Cited by 9 publications

References 3 publications

Investigating Effective Wayfinding in Airports: A Bayesian Network Approach

Investigating Effective Wayfinding in Airports: A Bayesian Network Approach

Hybrid Bayesian Network Models of Spinal Injury and Slip/Fall Events

Predicting the potential distribution of a riparian invasive plant: the effects of changing climate, flood regimes and land‐use patterns

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