# posterior.GaussianGaussian: Update a "GaussianGaussian" object with sample sufficient... In bbricks: Bayesian Methods and Graphical Model Structures for Statistical Modeling

## Description

For the model structure:

x \sim Gaussian(mu,Sigma)

mu \sim Gaussian(m,S)

Where Sigma is known. Gaussian() is the Gaussian distribution. See `?dGaussian` for the definition of Gaussian distribution.
Update (m,S) by adding the information of newly observed samples x. The model structure and prior parameters are stored in a "GaussianGaussian" object, the prior parameters in this object will be updated after running this function.

## Usage

 ```1 2``` ```## S3 method for class 'GaussianGaussian' posterior(obj, ss, ...) ```

## Arguments

 `obj` A "GaussianGaussian" object. `ss` Sufficient statistics of x. In Gaussian-Gaussian case the sufficient statistic of sample x is a object of type "ssGaussianMean", 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

Gelman, Andrew, et al. Bayesian data analysis. CRC press, 2013.

`GaussianGaussian`,`posteriorDiscard.GaussianGaussian`,`sufficientStatistics.GaussianGaussian`
 ```1 2 3 4 5 6``` ```obj <- GaussianGaussian(gamma=list(Sigma=matrix(c(2,1,1,2),2,2),m=c(0.2,0.5),S=diag(2))) obj x <- rGaussian(100,c(0,0),Sigma = matrix(c(2,1,1,2),2,2)) ss <- sufficientStatistics(obj=obj,x=x,foreach = FALSE) posterior(obj = obj,ss = ss) obj ```