Notation (
μ
,
Λ
)
∼
N
W
(
μ
0
,
λ
,
W
,
ν
)
{\displaystyle ({\boldsymbol {\mu }},{\boldsymbol {\Lambda }})\sim \mathrm {NW} ({\boldsymbol {\mu }}_{0},\lambda ,\mathbf {W} ,\nu )} Parameters μ
0
∈
R
D
{\displaystyle {\boldsymbol {\mu }}_{0}\in \mathbb {R} ^{D}\,}
location (vector of real)
λ
>
0
{\displaystyle \lambda >0\,}
(real)
W
∈
R
D
×
D
{\displaystyle \mathbf {W} \in \mathbb {R} ^{D\times D}}
scale matrix (pos. def.)
ν
>
D
−
1
{\displaystyle \nu >D-1\,}
(real) Support μ
∈
R
D
;
Λ
∈
R
D
×
D
{\displaystyle {\boldsymbol {\mu }}\in \mathbb {R} ^{D};{\boldsymbol {\Lambda }}\in \mathbb {R} ^{D\times D}}
covariance matrix (pos. def.) PDF f
(
μ
,
Λ
|
μ
0
,
λ
,
W
,
ν
)
=
N
(
μ
|
μ
0
,
(
λ
Λ
)
−
1
)
W
(
Λ
|
W
,
ν
)
{\displaystyle f({\boldsymbol {\mu }},{\boldsymbol {\Lambda }}|{\boldsymbol {\mu }}_{0},\lambda ,\mathbf {W} ,\nu )={\mathcal {N}}({\boldsymbol {\mu }}|{\boldsymbol {\mu }}_{0},(\lambda {\boldsymbol {\Lambda }})^{-1})\ {\mathcal {W}}({\boldsymbol {\Lambda }}|\mathbf {W} ,\nu )} |
In probability theory and statistics, the normal-Wishart distribution (or Gaussian-Wishart distribution) is a multivariate four-parameter family of continuous probability distributions. It is the conjugate prior of a multivariate normal distribution with unknown mean and precision matrix (the inverse of the covariance matrix).
Contents
Definition
Suppose
has a multivariate normal distribution with mean
has a Wishart distribution. Then
Probability density function
Marginal distributions
By construction, the marginal distribution over
Generating normal-Wishart random variates
Generation of random variates is straightforward:
- Sample
Λ from a Wishart distribution with parametersW andν - Sample
μ from a multivariate normal distribution with meanμ 0 ( λ Λ ) − 1