Initialised Dirichlet Process object

dpObj

Logical flag, defaults to <code>FAlSE</code>. Set whether the parameters of the base measure are updated.

updatePrior

Logical flag indicating whether to display a progress bar.

progressBar

Using Neal's algorithm 4 or 8 depending on conjugacy the sampling procedure for a Dirichlet process is carried out.
Lists of both cluster parameters, weights and the sampled concentration values are included in the fitted <code>dpObj</code>.
When <code>update_prior</code> is set to <code>TRUE</code> the parameters of the base measure are also updated.

Perform nonparametric Bayesian analysis using Dirichlet
processes without the need to program the inference algorithms.
Utilise included pre-built models or specify custom
models and allow the dirichletprocess package to handle the
Markov chain Monte Carlo sampling.
Our Dirichlet process objects can act as building blocks for a variety
of statistical models including and not limited to: density estimation,
clustering and prior distributions in hierarchical models.
See Teh, Y. W. (2011)
<https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>,
among many other sources.

Fit: Fit the Dirichlet process object

Description

Usage

Arguments

Value

References