scholarly journals Approximate Bayesian inference for a spatial point process model exhibiting regularity and random aggregation

2020 ◽  
Author(s):  
Ninna Vihrs ◽  
Jesper Møller ◽  
Alan E. Gelfand
Keyword(s):  
Bayesian Inference ◽  
Point Process ◽  
Process Model ◽  
Spatial Point Process ◽  
Point Process Model ◽  
Spatial Point ◽  
Approximate Bayesian ◽  
Random Aggregation ◽  
Approximate Bayesian Inference

scholarly journals A Spatial Point Process Model for Violent Tornado Occurrence in the US Great Plains

2013 ◽  
Vol 45 (6) ◽  
pp. 667-679 ◽  
Author(s):  
James B. Elsner ◽  
Richard J. Murnane ◽  
Thomas H. Jagger ◽  
Holly M. Widen
Keyword(s):  
Point Process ◽  
Great Plains ◽  
Process Model ◽  
Spatial Point Process ◽  
Point Process Model ◽  
Spatial Point ◽  
The Us ◽  
Us Great Plains

scholarly journals A Bayesian hierarchical spatial point process model for multi-type neuroimaging meta-analysis

2014 ◽  
Vol 8 (3) ◽  
pp. 1800-1824 ◽  
Author(s):  
Jian Kang ◽  
Thomas E. Nichols ◽  
Tor D. Wager ◽  
Timothy D. Johnson
Keyword(s):  
Point Process ◽  
Process Model ◽  
Meta Analysis ◽  
Spatial Point Process ◽  
Bayesian Hierarchical ◽  
Point Process Model ◽  
Spatial Point

scholarly journals Comparing two sequential Monte Carlo samplers for exact and approximate Bayesian inference on biological models

2017 ◽  
Vol 14 (134) ◽  
pp. 20170340 ◽  
Author(s):  
Aidan C. Daly ◽  
Jonathan Cooper ◽  
David J. Gavaghan ◽  
Chris Holmes
Keyword(s):  
Bayesian Inference ◽  
Bayesian Methods ◽  
Sequential Monte Carlo ◽  
Model Parameters ◽  
Biological Models ◽  
Exact Inference ◽  
Modelling Studies ◽  
Approximate Bayesian ◽  
Approximate Bayesian Inference ◽  
Abc Methods

Bayesian methods are advantageous for biological modelling studies due to their ability to quantify and characterize posterior variability in model parameters. When Bayesian methods cannot be applied, due either to non-determinism in the model or limitations on system observability, approximate Bayesian computation (ABC) methods can be used to similar effect, despite producing inflated estimates of the true posterior variance. Owing to generally differing application domains, there are few studies comparing Bayesian and ABC methods, and thus there is little understanding of the properties and magnitude of this uncertainty inflation. To address this problem, we present two popular strategies for ABC sampling that we have adapted to perform exact Bayesian inference, and compare them on several model problems. We find that one sampler was impractical for exact inference due to its sensitivity to a key normalizing constant, and additionally highlight sensitivities of both samplers to various algorithmic parameters and model conditions. We conclude with a study of the O'Hara–Rudy cardiac action potential model to quantify the uncertainty amplification resulting from employing ABC using a set of clinically relevant biomarkers. We hope that this work serves to guide the implementation and comparative assessment of Bayesian and ABC sampling techniques in biological models.

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