Parallel Exact Inference on a CPU-GPGPU Heterogenous System

2010 ◽  
Author(s):  
Hyeran Jeon ◽  
Yinglong Xia ◽  
Viktor K. Prasanna
Keyword(s):  
Exact Inference ◽  
Heterogenous System

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 Exact inference on the random‐effects model for meta‐analyses with few studies

Biometrics ◽  
2019 ◽  
Vol 75 (2) ◽  
pp. 485-493
Author(s):  
Haben Michael ◽  
Suzanne Thornton ◽  
Minge Xie ◽  
Lu Tian
Keyword(s):  
Random Effects ◽  
Random Effects Model ◽  
Exact Inference ◽  
Meta Analyses

Exact inference for adaptive group sequential designs

2013 ◽  
Vol 32 (23) ◽  
pp. 3991-4005 ◽  
Author(s):  
Ping Gao ◽  
Lingyun Liu ◽  
Cyrus Mehta
Keyword(s):  
Sequential Designs ◽  
Group Sequential ◽  
Exact Inference ◽  
Group Sequential Designs

scholarly journals [A Survey of Exact Inference for Contingency Tables]: Comment

1992 ◽  
Vol 7 (1) ◽  
pp. 160-162 ◽  
Author(s):  
Leonardo D. Epstein ◽  
Stephen E. Fienberg
Keyword(s):  
Contingency Tables ◽  
Exact Inference

scholarly journals Improved High Dimensional Discrete Bayesian Network Inference using Triplet Region Construction

2020 ◽  
Vol 69 ◽  
pp. 231-295
Author(s):  
Peng Lin ◽  
Martin Neil ◽  
Norman Fenton
Keyword(s):  
Network Inference ◽  
General Purpose ◽  
Space Complexity ◽  
High Dimensional ◽  
Choice Problem ◽  
Worst Case ◽  
Exact Inference ◽  
Tree Width ◽  
Inference Methods ◽  
Bayesian Network Inference

Performing efficient inference on high dimensional discrete Bayesian Networks (BNs) is challenging. When using exact inference methods the space complexity can grow exponentially with the tree-width, thus making computation intractable. This paper presents a general purpose approximate inference algorithm, based on a new region belief approximation method, called Triplet Region Construction (TRC). TRC reduces the cluster space complexity for factorized models from worst-case exponential to polynomial by performing graph factorization and producing clusters of limited size. Unlike previous generations of region-based algorithms, TRC is guaranteed to converge and effectively addresses the region choice problem that bedevils other region-based algorithms used for BN inference. Our experiments demonstrate that it also achieves significantly more accurate results than competing algorithms.

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