scholarly journals A Memory Constrained Approximate Bayesian Inference Approach Using Incremental Construction of Clique Trees

2021 ◽  
Author(s):  
Shivani Bathla ◽  
Vinita Vasudevan
Keyword(s):  
Bayesian Inference ◽  
Belief Propagation ◽  
Maximum Clique ◽  
Approximate Inference ◽  
Clique Trees ◽  
Approximate Bayesian ◽  
Incremental Construction ◽  
Approximate Bayesian Inference ◽  
Memory Constraints

<div>The complexity of inference using the belief propagation algorithms increases exponentially with the maximum clique size. We describe an approximate inference approach when there are clique size limitations due to memory constraints using incremental construction of clique trees.<br></div>

scholarly journals A Memory Constrained Approximate Bayesian Inference Approach Using Incremental Construction of Clique Trees

2021 ◽  
Author(s):  
Shivani Bathla ◽  
Vinita Vasudevan
Keyword(s):  
Bayesian Inference ◽  
Belief Propagation ◽  
Maximum Clique ◽  
Approximate Inference ◽  
Clique Trees ◽  
Approximate Bayesian ◽  
Incremental Construction ◽  
Approximate Bayesian Inference ◽  
Memory Constraints

<div>The complexity of inference using the belief propagation algorithms increases exponentially with the maximum clique size. We describe an approximate inference approach when there are clique size limitations due to memory constraints using incremental construction of clique trees.<br></div>

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.

scholarly journals Approximate Bayesian inference in semi-mechanistic models

2016 ◽  
Vol 27 (4) ◽  
pp. 1003-1040 ◽  
Author(s):  
Andrej Aderhold ◽  
Dirk Husmeier ◽  
Marco Grzegorczyk
Keyword(s):  
Bayesian Inference ◽  
Mechanistic Models ◽  
Approximate Bayesian ◽  
Approximate Bayesian Inference

On fitting spatio-temporal disease mapping models using approximate Bayesian inference

2014 ◽  
Vol 23 (6) ◽  
pp. 507-530 ◽  
Author(s):  
María Dolores Ugarte ◽  
Aritz Adin ◽  
Tomas Goicoa ◽  
Ana Fernandez Militino
Keyword(s):  
Bayesian Inference ◽  
Disease Mapping ◽  
Spatio Temporal ◽  
Approximate Bayesian ◽  
Mapping Models ◽  
Approximate Bayesian Inference
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