# 威沙特分布

参数 ${\displaystyle n>0\!}$ 自由度 (实数)${\displaystyle \mathbf {V} >0\,}$ 尺度矩阵 (正定) ${\displaystyle \mathbf {W} \!}$是正定的 ${\displaystyle {\frac {\left|\mathbf {W} \right|^{\frac {n-p-1}{2}}}{2^{\frac {np}{2}}\left|{\mathbf {V} }\right|^{\frac {n}{2}}\Gamma _{p}({\frac {n}{2}})}}\exp \left(-{\frac {1}{2}}{\rm {Tr}}({\mathbf {V} }^{-1}\mathbf {W} )\right)}$ ${\displaystyle n\mathbf {V} }$ ${\displaystyle (n-p-1)\mathbf {V} {\text{ for }}n\geq p+1}$ ${\displaystyle \Theta \mapsto \left|{\mathbf {I} }-2i\,{\mathbf {\Theta } }{\mathbf {V} }\right|^{-n/2}}$

## 定义

${\displaystyle X_{(i)}{=}(x_{i}^{1},\dots ,x_{i}^{p})^{T}\sim N_{p}(0,V),}$

${\displaystyle S=X^{T}X=\sum _{i=1}^{n}X_{(i)}X_{(i)}^{T},\,\!}$

${\displaystyle \mathbf {S} }$有该几率分布通常记为

${\displaystyle \mathbf {S} \sim W_{p}(\mathbf {V} ,n).}$

## 几率密度函数

${\displaystyle f_{\mathbf {W} }(w)={\frac {\left|w\right|^{(n-p-1)/2}\exp \left[-{\rm {trace}}({\mathbf {V} }^{-1}w/2)\right]}{2^{np/2}\left|{\mathbf {V} }\right|^{n/2}\Gamma _{p}(n/2)}}}$

${\displaystyle \Gamma _{p}(n/2)=\pi ^{p(p-1)/4}\Pi _{j=1}^{p}\Gamma \left[(n+1-j)/2\right].}$

## 特征函数

${\displaystyle \Theta \mapsto \left|{\mathbf {I} }-2i\,{\mathbf {\Theta } }{\mathbf {V} }\right|^{-n/2}.}$

${\displaystyle \Theta \mapsto {\mathcal {E}}\left\{\mathrm {exp} \left[i\cdot \mathrm {trace} ({\mathbf {W} }{\mathbf {\Theta } })\right]\right\}=\left|{\mathbf {I} }-2i{\mathbf {\Theta } }{\mathbf {V} }\right|^{-n/2}}$

（这里的${\displaystyle \Theta }$${\displaystyle {\mathbf {I} }}$ 皆为与${\displaystyle {\mathbf {V} }}$维度相同的矩阵。（${\displaystyle {\mathbf {I} }}$单位矩阵，而${\displaystyle i}$为－1的平方根）.

## 理论架构

${\displaystyle \scriptstyle {\mathbf {W} }}$为一自由度为m，共变异矩阵为${\displaystyle \scriptstyle {\mathbf {V} }}$的威沙特分布，记为—${\displaystyle \scriptstyle {\mathbf {W} }\sim {\mathbf {W} }_{p}({\mathbf {V} },m)}$—其中${\displaystyle \scriptstyle {\mathbf {C} }}$为一${\displaystyle q\times p}$q秩矩阵，则

${\displaystyle {\mathbf {C} }{\mathbf {W} }{\mathbf {C} '}\sim {\mathbf {W} }_{q}\left({\mathbf {C} }{\mathbf {V} }{\mathbf {C} '},m\right).}$

### 推论1

${\displaystyle {\mathbf {z} }}$为一非负${\displaystyle p\times 1}$常数向量，则 ${\displaystyle {\mathbf {z} '}{\mathbf {W} }{\mathbf {z} }\sim \sigma _{z}^{2}\chi _{m}^{2}}$.

### 推论2

${\displaystyle {\mathbf {z} '}=(0,\ldots ,0,1,0,\ldots ,0)}$ 的情形下（亦即第j个元素为1其他为0），推论1可导出

${\displaystyle w_{jj}\sim \sigma _{jj}\chi _{m}^{2}}$

## 分布抽样

1. 生成一随机${\displaystyle p\times p}$三角矩阵 ${\displaystyle {\textbf {A}}}$使得：
• ${\displaystyle a_{ii}=(\chi _{n-i+1}^{2})^{1/2}}$，意即 ${\displaystyle a_{ii}}$为一${\displaystyle \chi _{n-i+1}^{2}}$卡方分布随机样本的平方根。
• ${\displaystyle a_{ij}}$其中${\displaystyle j，为一${\displaystyle N_{1}(0,1)}$正态分布的随机样本。
2. 计算${\displaystyle {\textbf {V}}={\textbf {L}}{\textbf {L}}^{T}}$Cholesky分解
3. 计算${\displaystyle {\textbf {X}}={\textbf {L}}{\textbf {A}}{\textbf {A}}^{T}{\textbf {L}}^{T}}$。此时，${\displaystyle {\textbf {X}}}$ 为一${\displaystyle W_{p}({\textbf {V}},n)}$的随机样本。

${\displaystyle {\textbf {V}}={\textbf {I}}}$，则因${\displaystyle {\textbf {V}}={\textbf {I}}{\textbf {I}}^{T}}$，可以直接以${\displaystyle {\textbf {X}}={\textbf {A}}{\textbf {A}}^{T}}$进行抽样。

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