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.
