# MAP.GaussianNIG: Maximum A Posteriori (MAP) estimate of a "GaussianNIG" object In bbricks: Bayesian Methods and Graphical Model Structures for Statistical Modeling

## Description

Generate the MAP estimate of (beta,sigma^2) in following Gaussian-NIG 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.
The MAP estimates are:

• (beta,sigma^2)_MAP = argmax p(beta,sigma^2|m,V,a,b,x,X)

## Usage

 ```1 2``` ```## S3 method for class 'GaussianNIG' MAP(obj, ...) ```

## Arguments

 `obj` A "GaussianNIG" object. `...` Additional arguments to be passed to other inherited types.

## Value

A named list, the MAP estimate of beta and sigma^2.

## References

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

`GaussianNIG`
 ```1 2 3 4 5 6``` ```obj <- GaussianNIG(gamma=list(m=0,V=1,a=1,b=1)) X <- 1:20 x <- rnorm(20)+ X*0.3 ss <- sufficientStatistics(obj = obj,X=X,x=x) posterior(obj = obj,ss = ss) MAP(obj) ```