bbricks (version 0.1.4)

posterior.GaussianNIG: Update a "GaussianNIG" object with sample sufficient statistics

Description

For the 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. Update (m,V,a,b) by adding the information of newly observed samples (x,X). The model structure and prior parameters are stored in a "GaussianNIG" object, the prior parameters in this object will be updated after running this function.

Usage

# S3 method for GaussianNIG
posterior(obj, ss, ...)

Arguments

obj

A "GaussianNIG" object.

ss

Sufficient statistics of (x,X). In Gaussian-NIG case the sufficient statistic of sample (x,X) is a object of type "ssGaussianLinear", it can be generated by the function sufficientStatistics().

...

Additional arguments to be passed to other inherited types.

Value

None. the gamma stored in "obj" will be updated based on "ss".

References

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

See Also

GaussianNIG,posteriorDiscard.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
ss <- sufficientStatistics(obj = obj,X=X,x=x)
posterior(obj = obj,ss = ss)
obj
# }