bayesestdft
Estimating the Degrees of Freedom of the Student's t-Distribution under a Bayesian Framework
A Bayesian framework to estimate the Student's t-distribution's degrees of freedom is developed. Markov Chain Monte Carlo sampling routines are developed as in <doi:10.3390/axioms11090462> to sample from the posterior distribution of the degrees of freedom. A random walk Metropolis algorithm is used for sampling when Jeffrey's and Gamma priors are endowed upon the degrees of freedom. In addition, the Metropolis-adjusted Langevin algorithm for sampling is used under the Jeffrey's prior specification. The Log-normal prior over the degrees of freedom is posed as a viable choice with comparable performance in simulations and real-data application, against other prior choices, where an Elliptical Slice Sampler is used to sample from the concerned posterior.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.0 |
rolling linux/jammy R-4.5 | bayesestdft_1.0.0.tar.gz |
866.7 KiB |
1.0.0 |
rolling linux/noble R-4.5 | bayesestdft_1.0.0.tar.gz |
866.6 KiB |
1.0.0 |
rolling source/ R- | bayesestdft_1.0.0.tar.gz |
853.1 KiB |
1.0.0 |
latest linux/jammy R-4.5 | bayesestdft_1.0.0.tar.gz |
866.7 KiB |
1.0.0 |
latest linux/noble R-4.5 | bayesestdft_1.0.0.tar.gz |
866.6 KiB |
1.0.0 |
latest source/ R- | bayesestdft_1.0.0.tar.gz |
853.1 KiB |
1.0.0 |
2026-04-26 source/ R- | bayesestdft_1.0.0.tar.gz |
853.1 KiB |
1.0.0 |
2026-04-23 source/ R- | bayesestdft_1.0.0.tar.gz |
853.1 KiB |
1.0.0 |
2026-04-09 windows/windows R-4.5 | bayesestdft_1.0.0.zip |
870.9 KiB |
1.0.0 |
2025-04-20 source/ R- | bayesestdft_1.0.0.tar.gz |
853.1 KiB |