bbricks (version 0.1.4)

sufficientStatistics_Weighted.GaussianNIG: Weighted sufficient statistics of a "GaussianNIG" object

Description

For following Gaussian-NIG model structure: $$x \sim Gaussian(X beta,sigma^2)$$ $$sigma^2 \sim InvGamma(a,b)$$ $$beta \sim Gaussian(m,sigma^2 V)$$ Where X is a row vector, or a design matrix where each row is an obervation. InvGamma() is the Inverse-Gamma distribution, Gaussian() is the Gaussian distribution. See ?dInvGamma and dGaussian for the definitions of these distribution. The model structure and prior parameters are stored in a "GaussianNIG" object. This object will be used as a place for recording and accumulating information in the related inference/sampling functions such as posterior(), posteriorDiscard(), MAP(), marginalLikelihood(), dPosteriorPredictive(), rPosteriorPredictive() and so on. The sufficient statistics of a set of samples (x,X) and weights ware:

• the effective number of samples N=sum(w);

• the covariance of X and x SXx=t(w*X)

• the covariance of X SX=t(w*X)

• the covariance of x Sx=t(w*x)

Usage

# S3 method for GaussianNIG
sufficientStatistics_Weighted(obj, x, w, X, foreach = FALSE, ...)

Arguments

obj

A "GaussianNIG" object.

x

numeric, must satisfy length(x) = nrow(X).

w

numeric, sample weights.

X

matrix, must satisfy length(x) = nrow(X).

foreach

logical, if foreach=TRUE, will return a list of sufficient statistics for each (x,X), otherwise will return the sufficient statistics as a whole.

...

Additional arguments to be passed to other inherited types.

Value

If foreach=TRUE, will return a list of sufficient statistics for each row of (x,X), otherwise will return the sufficient statistics of (x,X) as a whole.

References

Banerjee, Sudipto. "Bayesian Linear Model: Gory Details." Downloaded from http://www. biostat. umn. edu/~ph7440 (2008).

See Also

GaussianNIG, sufficientStatistics.GaussianNIG

Examples

# NOT RUN {
obj <- GaussianNIG(gamma=list(m=0,V=1,a=1,b=0))
X <- 1:20
x <- rnorm(20)+ X*0.3
w <- runif(20)
sufficientStatistics_Weighted(obj = obj,X=X,x=x,w=w)
sufficientStatistics_Weighted(obj = obj,X=X,x=x,w=w,foreach = TRUE)
# }