posterior.GaussianGaussian {bbricks} R Documentation

## Update a "GaussianGaussian" object with sample sufficient statistics

### 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

```## 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`

### Examples

```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
```

[Package bbricks version 0.1.4 Index]