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This is a method (MinED) for mining probability distributions using deterministic sampling which is proposed by Joseph, Wang, Gu, Lv, and Tuo (2019) <doi:10.1080/00401706.2018.1552203>. The MinED samples can be used for approximating the target distribution. They can be generated from a density function that is known only up to a proportionality constant and thus, it might find applications in Bayesian computation. Moreover, the MinED samples are generated with much fewer evaluations of the density function compared to random sampling-based methods such as MCMC and therefore, this method will be especially useful when the unnormalized posterior is expensive or time consuming to evaluate. This research is supported by a U.S. National Science Foundation grant DMS-1712642.
| Version: | 1.0-3 |
| Imports: | Rcpp (≥ 0.12.17) |
| LinkingTo: | Rcpp, RcppEigen |
| Published: | 2022-06-26 |
| Author: | Dianpeng Wang and V. Roshan Joseph |
| Maintainer: | Dianpeng Wang <wdp at bit.edu.cn> |
| License: | LGPL-2.1 |
| NeedsCompilation: | yes |
| CRAN checks: | mined results |
| Reference manual: | mined.pdf |
| Package source: | mined_1.0-3.tar.gz |
| Windows binaries: | r-devel: mined_1.0-3.zip, r-release: mined_1.0-3.zip, r-oldrel: mined_1.0-3.zip |
| macOS binaries: | r-release (arm64): mined_1.0-3.tgz, r-oldrel (arm64): mined_1.0-3.tgz, r-release (x86_64): mined_1.0-3.tgz, r-oldrel (x86_64): mined_1.0-3.tgz |
| Old sources: | mined archive |
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