使用 python 的多元学生 t 分布 [英] multivariate student t-distribution with python

问题描述

要生成具有多元 t 分布的样本，我使用此函数:

To generate samples with multivariate t-distribution I use this function:

def multivariatet(mu,Sigma,N,M):
    '''
    Output:
    Produce M samples of d-dimensional multivariate t distribution
    Input:
    mu = mean (d dimensional numpy array or scalar)
    Sigma = scale matrix (dxd numpy array)
    N = degrees of freedom
    M = # of samples to produce
    '''
    d = len(Sigma)
    g = np.tile(np.random.gamma(N/2.,2./N,M),(d,1)).T
    Z = np.random.multivariate_normal(np.zeros(d),Sigma,M)
    return mu + Z/np.sqrt(g)

但我现在正在寻找的是 多元学生 t-distribution 本身所以我可以计算元素的密度，其中 dimension >1.

but what I am looking for now is the multivariate student t-distribution it self so I can calculate the density of elements where dimension > 1.

这将类似于scipy 但在多维空间中.

That will be something like stats.t.pdf(x, df, loc, scale) of the package scipy but in multi-dimensional space.

推荐答案

我自己编码了密度:

import numpy as np
from math import *

def multivariate_t_distribution(x,mu,Sigma,df,d):
    '''
    Multivariate t-student density:
    output:
        the density of the given element
    input:
        x = parameter (d dimensional numpy array or scalar)
        mu = mean (d dimensional numpy array or scalar)
        Sigma = scale matrix (dxd numpy array)
        df = degrees of freedom
        d: dimension
    '''
    Num = gamma(1. * (d+df)/2)
    Denom = ( gamma(1.*df/2) * pow(df*pi,1.*d/2) * pow(np.linalg.det(Sigma),1./2) * pow(1 + (1./df)*np.dot(np.dot((x - mu),np.linalg.inv(Sigma)), (x - mu)),1.* (d+df)/2))
    d = 1. * Num / Denom 
    return d

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