| Stick {LaplacesDemon} | R Documentation |
Truncated Stick-Breaking
Description
The Stick function provides the utility of truncated
stick-breaking regarding the vector
\theta. Stick-breaking is commonly referred to as a
stick-breaking process, and is used often in a Dirichlet
process (Sethuraman, 1994). It is commonly associated with
infinite-dimensional mixtures, but in practice, the ‘infinite’ number
is truncated to a finite number, since it is impossible to estimate an
infinite number of parameters (Ishwaran and James, 2001).
Usage
Stick(theta)
Arguments
theta |
This required argument, |
Details
The Dirichlet process (DP) is a stochastic process used in Bayesian nonparametric modeling, most commonly in DP mixture models, otherwise known as infinite mixture models. A DP is a distribution over distributions. Each draw from a DP is itself a discrete distribution. A DP is an infinite-dimensional generalization of Dirichlet distributions. It is called a DP because it has Dirichlet-distributed, finite-dimensional, marginal distributions, just as the Gaussian process has Gaussian-distributed, finite-dimensional, marginal distributions. Distributions drawn from a DP cannot be described using a finite number of parameters, thus the classification as a nonparametric model. The truncated stick-breaking (TSB) process is associated with a truncated Dirichlet process (TDP).
An example of a TSB process is cluster analysis, where the number of
clusters is unknown and treated as mixture components. In such a
model, the TSB process calculates probability vector \pi
from \theta, given a user-specified maximum number of
clusters to explore as C, where C is the length of
\theta + 1. Vector \pi is assigned a TSB
prior distribution (for more information, see dStick).
Elsewhere, each element of \theta is constrained to the
interval (0,1), and the original TSB form is beta-distributed with the
\alpha parameter of the beta distribution constrained
to 1 (Ishwaran and James, 2001). The \beta hyperparameter
in the beta distribution is usually gamma-distributed.
A larger value for a given \theta_m is associated
with a higher probability of the associated mixture component,
however, the proportion changes according to the position of the
element in the \theta vector.
A variety of stick-breaking processes exist. For example, rather than
each \theta being beta-distributed, there have been other
forms introduced such as logistic and probit, among others.
Value
The Stick function returns a probability vector wherein each
element relates to a mixture component.
Author(s)
Statisticat, LLC. software@bayesian-inference.com
References
Ishwaran, H. and James, L. (2001). "Gibbs Sampling Methods for Stick Breaking Priors". Journal of the American Statistical Association, 96(453), p. 161–173.
Sethuraman, J. (1994). "A Constructive Definition of Dirichlet Priors". Statistica Sinica, 4, p. 639–650.
See Also
ddirichlet,
dmvpolya, and
dStick.