Applied Bayesian Modelling

Congdon, Peter

doi:10.1002/9781118895047

Cited by 108 publications

(87 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, uncertainties are generally lacking from 336 most SDM reports (Congdon 2003;Clark & Gelfand 2006), even if the approach used 337 can produce them. Species distribution maps generated with low quality data (e.g.…”

Section: Discussion 314mentioning

confidence: 99%

“…Bernoulli(p i ), The spatially expanded model (Casetti 1997;Congdon 2003) assumes that 217 the effect of an explanatory variable on the response variable p i can vary among the 218 observed locations. This assumption is particularly convenient when fitting species 219 distribution model along large ranges, where the species can be locally adapted to e.g.…”

Section: Modelling the Species Distribution 200mentioning

confidence: 99%

See 1 more Smart Citation

Goal-oriented evaluation of species distribution models’ accuracy and precision: True Skill Statistic profile and uncertainty maps

Ruete

Leynaud

2015

Preprint

View full text Add to dashboard Cite

The use of species distribution models’ (SDM) is limited by its performance in terms of accuracy, precision, or the spatial distribution of model errors. Despite the wide acceptance of some standard statistics used to evaluate SDM, there is currently a strong on-going debate as to their use. The “area under the curve” (AUC) is a popular measure used to evaluate SDMs; however, it does not provide complete information about model accuracy. The maximum True Skill Statistic (TSS) is another statistic that is gaining acceptance. However, evaluations of a model’s accuracy solely based on this statistic may also be misleading. We investigate the use of alternative methods to evaluate the performance of SDMs, to objectively compare among different modelling approaches. We evaluate the performance of SDMs fitted to simulated and real data by contrasting model predictions to additional validation datasets. We propose visualising TSS scores over the whole detection threshold range (TSS profile). We show how models with similarly good performance according to AUC, present very different results and may serve to different purposes. Also, a high maximum TSS may not guarantee accurate predictions and should be accompanied by the threshold where the maximum is reached (t*). We observe that the higher t* the better predicted observations correlate with confirmed observations. Also, SDM predictions should be accompanied with the corresponding uncertainty map to avoid misleading conclusions. Too high or too widely spread uncertainty on such maps would question the overall accuracy of the model. Whether the model is intended to detect all potential observation sites (sensitive model) or to accurately predict where confirmed observations could be found (specific model) sets a different performance targets to be achieved by the model. The approach proposed helps to discern which SDM may best suit the intended goals. Furthermore, the TSS profile helps i) to evaluate the overall performance of SDMs and compare among them, ii) to identify the main source of error, and iii) to select a detection threshold depending on the maps intended use.

show abstract

Section: Discussion 314mentioning

confidence: 99%

Section: Modelling the Species Distribution 200mentioning

confidence: 99%

Goal-oriented evaluation of species distribution models’ accuracy and precision: True Skill Statistic profile and uncertainty maps

Ruete

Leynaud

2015

Preprint

View full text Add to dashboard Cite

show abstract

“…The Deviance Information Criterion (DIC) was used to assess model fit [57]. Broadly, DIC is a Bayesian analogue of the Akaike Information Criterion and balances model goodness-of-fit and complexity [58]. Smaller DIC values by at least five units indicate superior model fit [57].…”

Section: Model Implementation and Goodness-of-fitmentioning

confidence: 99%

Analyzing Local Spatio-Temporal Patterns of Police Calls-for-Service Using Bayesian Integrated Nested Laplace Approximation

Luan

Quick

Law

2016

IJGI

View full text Add to dashboard Cite

This research investigates spatio-temporal patterns of police calls-for-service in the Region of Waterloo, Canada, at a fine spatial and temporal resolution. Modeling was implemented via Bayesian Integrated Nested Laplace Approximation (INLA). Temporal patterns for two-hour time periods, spatial patterns at the small-area scale, and space-time interaction (i.e., unusual departures from overall spatial and temporal patterns) were estimated. Temporally, calls-for-service were found to be lowest in the early morning (02:00-03:59) and highest in the evening (20:00-21:59), while high levels of calls-for-service were spatially located in central business areas and in areas characterized by major roadways, universities, and shopping centres. Space-time interaction was observed to be geographically dispersed during daytime hours but concentrated in central business areas during evening hours. Interpreted through the routine activity theory, results are discussed with respect to law enforcement resource demand and allocation, and the advantages of modeling spatio-temporal datasets with Bayesian INLA methods are highlighted.

show abstract

“…Its use for 'within-game' analysis is relatively new, though Glickman and Stern (1998), Knorr-Held (2000), Crowder et al (2002) and Owen (2011) considered applications to 'between-game' situations. Furthermore, Congdon (2003) presented an accessible account of Bayesian inference for several dynamic models, including the time-series models that we review and apply here. An interesting aspect of this updating procedure arises when there is a choice of time interval for re-defining the available set of observed data.…”

Section: Dynamic Learningmentioning

confidence: 99%

Strategy selection and outcome prediction in sport using dynamic learning for stochastic processes

Percy

2015

Journal of the Operational Research Society

View full text Add to dashboard Cite

Stochastic processes are natural models for the progression of many individual and team sports. Such models have been applied successfully to select strategies and to predict outcomes in the context of games, tournaments and leagues. This information is useful to participants and gamblers, who often need to make decisions while the sports are in progress. In order to apply these models, much of the published research uses parameters estimated from historical data, thereby ignoring the uncertainty of the parameter values and the most relevant information that arises during competition. In this paper, we investigate candidate stochastic processes for familiar sporting applications that include cricket, football and badminton, reviewing existing models and offering some new suggestions. We then consider how to model parameter uncertainty with prior and posterior distributions, how to update these distributions dynamically during competition and how to use these results to make optimal decisions. Finally, we combine these ideas in a case study aimed at predicting the winners of next year's University Boat Race.

show abstract

Applied Bayesian Modelling

Cited by 108 publications

References 0 publications

Goal-oriented evaluation of species distribution models’ accuracy and precision: True Skill Statistic profile and uncertainty maps

Goal-oriented evaluation of species distribution models’ accuracy and precision: True Skill Statistic profile and uncertainty maps

Analyzing Local Spatio-Temporal Patterns of Police Calls-for-Service Using Bayesian Integrated Nested Laplace Approximation

Strategy selection and outcome prediction in sport using dynamic learning for stochastic processes

Contact Info

Product

Resources

About