This article describes advances in statistical computation for large-scale data analy-sis in structured Bayesian mixture models via graphics processing unit (GPU) pro-gramming. The developments are partly motivated by computational challenges arising in fitting models of increasing heterogeneity to increasingly large datasets. An example context concerns common biological studies using high-throughput technologies gen-erating many, very large datasets and requiring increasingly high-dimensional mixture models with large numbers of mixture components. We outline important strategies and processes for GPU computation in Bayesian simulation and optimization approaches, give examples of the benefits of GPU implementations in terms of processing...
MrBayes is model-based phylogenetic inference tool using Bayesian statistics. However, model-based a...
Many tasks in data mining and statistics are inherently parallel. While modern commodity desktop pro...
Big Bayes is the computationally intensive co-application of big data and large, expressive Bayesian...
We describe the integration of Bayesian non-parametric mixture models, massively parallel computing ...
The integration of multi-dimensional datasets remains a key challenge in systems biology and genomic...
Abstract: The graphics processing unit (GPU) has emerged as a power-ful and cost effective processor...
We describe an efficient Bayesian parallel GPU implementation of two classic statistical models-the ...
Many modern-day Bioinformatics algorithms rely heavily on statistical models to analyze their biolog...
A graphical processing unit (GPU) is a hardware device normally used to manipulate computer memory f...
As the processing power available in computers grows, so do the applications for using that power fo...
This paper presents a heterogeneous computing solution for an optimized genetic selection analysis t...
In this project we develop Graphics Processing Unit (GPU) based tools to address the statistical com...
Background: Over the last years, substantial effort has been put into enhancing our arsenal in fight...
The computational demands of multivariate clustering grow rapidly, and therefore processing large da...
Ecological modelling can allow us to test hypotheses and answer questions in cases where empirical e...
MrBayes is model-based phylogenetic inference tool using Bayesian statistics. However, model-based a...
Many tasks in data mining and statistics are inherently parallel. While modern commodity desktop pro...
Big Bayes is the computationally intensive co-application of big data and large, expressive Bayesian...
We describe the integration of Bayesian non-parametric mixture models, massively parallel computing ...
The integration of multi-dimensional datasets remains a key challenge in systems biology and genomic...
Abstract: The graphics processing unit (GPU) has emerged as a power-ful and cost effective processor...
We describe an efficient Bayesian parallel GPU implementation of two classic statistical models-the ...
Many modern-day Bioinformatics algorithms rely heavily on statistical models to analyze their biolog...
A graphical processing unit (GPU) is a hardware device normally used to manipulate computer memory f...
As the processing power available in computers grows, so do the applications for using that power fo...
This paper presents a heterogeneous computing solution for an optimized genetic selection analysis t...
In this project we develop Graphics Processing Unit (GPU) based tools to address the statistical com...
Background: Over the last years, substantial effort has been put into enhancing our arsenal in fight...
The computational demands of multivariate clustering grow rapidly, and therefore processing large da...
Ecological modelling can allow us to test hypotheses and answer questions in cases where empirical e...
MrBayes is model-based phylogenetic inference tool using Bayesian statistics. However, model-based a...
Many tasks in data mining and statistics are inherently parallel. While modern commodity desktop pro...
Big Bayes is the computationally intensive co-application of big data and large, expressive Bayesian...