sufficientStatistics_Weighted.DP {bbricks} | R Documentation |
For following model structure:
pi|alpha \sim DP(alpha,U)
z|pi \sim Categorical(pi)
theta_z|psi \sim H0(psi)
x|theta_z,z \sim F(theta_z)
where DP(alpha,U) is a Dirichlet Process on positive integers, alpha is the "concentration parameter" of the Dirichlet Process, U is the "base measure" of this Dirichlet process. The choice of F() and H0() can be described by an arbitrary "BasicBayesian" object such as "GaussianGaussian","GaussianInvWishart","GaussianNIW", "GaussianNIG", "CatDirichlet", and "CatDP". See ?BasicBayesian
for definition of "BasicBayesian" objects, and see for example ?GaussianGaussian
for specific "BasicBayesian" instances. As a summary, An "DP" object is simply a combination of a "CatDP" object (see ?CatDP
) and an object of any "BasicBayesian" type.
The sufficient statistics of a set of samples x in a "DP" object is the same sufficient statistics of the "BasicBayesian" inside the "DP", see examples.
## S3 method for class 'DP' sufficientStatistics_Weighted(obj, x, w, ...)
obj |
A "DP" object. |
x |
Random samples of the "BasicBayesian" object. |
w |
numeric, sample weights. |
... |
Additional arguments to be passed to other inherited types. |
Return the sufficient statistics of the corresponding BasicBayesian type, see examples.
Teh, Yee W., et al. "Sharing clusters among related groups: Hierarchical Dirichlet processes." Advances in neural information processing systems. 2005.
obj1 <- DP(gamma=list(alpha=1,H0aF="GaussianNIW",parH0=list(m=1,k=1,v=1,S=1))) obj2 <- DP(gamma=list(alpha=1,H0aF="CatDirichlet",parH0=list(alpha=1,uniqueLabels=letters))) x1 <- rnorm(100) x2 <- sample(letters,100,replace = TRUE) w <- runif(100) sufficientStatistics_Weighted(obj = obj1,x=x1,w=w) sufficientStatistics_Weighted(obj = obj2,x=x2,w=w) sufficientStatistics_Weighted(obj = obj1,x=x1,w=w,foreach = TRUE)