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GPU-accelerated hierarchical Bayesian estimation of luminosity functions using flux-limited observations with photometric noise
- Source :
- Astronomy and Computing. 25:247-256
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
Abstract
- We describe a computational framework for hierarchical Bayesian inference with simple (typically single-plate) parametric graphical models that uses graphics processing units (GPUs) to accelerate computations, enabling deployment on very large datasets. Its C++ implementation, CUDAHM (CUDA for Hierarchical Models) exploits conditional independence between instances of a plate, facilitating massively parallel exploration of the replication parameter space using the single instruction, multiple data architecture of GPUs. It provides support for constructing Metropolis-within-Gibbs samplers that iterate between GPU-accelerated robust adaptive Metropolis sampling of plate-level parameters conditional on upper-level parameters, and Metropolis-Hastings sampling of upper-level parameters on the host processor conditional on the GPU results. CUDAHM is motivated by demographic problems in astronomy, where density estimation and linear and nonlinear regression problems must be addressed for populations of thousands to millions of objects whose features are measured with possibly complex uncertainties. We describe a thinned latent point process framework for modeling such demographic data. We demonstrate accurate GPU-accelerated parametric conditional density deconvolution for simulated populations of up to 300,000 objects in ~1 hour using a single NVIDIA Tesla K40c GPU. Supplementary material provides details about the CUDAHM API and the demonstration problem.
- Subjects :
- Computer science
Astronomy and Astrophysics
Density estimation
Conditional probability distribution
Bayesian inference
01 natural sciences
Computer Science Applications
Computational science
010104 statistics & probability
CUDA
Conditional independence
Space and Planetary Science
0103 physical sciences
Graphical model
0101 mathematics
010303 astronomy & astrophysics
Massively parallel
Parametric statistics
Subjects
Details
- ISSN :
- 22131337
- Volume :
- 25
- Database :
- OpenAIRE
- Journal :
- Astronomy and Computing
- Accession number :
- edsair.doi...........0613a3a962a682e9f1797c04b8f9b9fb