Question

我正在使用pymc3（具有大量先验的图形模型）进行贝叶斯分析，如下所示：

import pymc3 as pm
import theano.tensor as T
with pm.Model() as smk:
    a_u = pm.InverseGamma('a_u',alpha=2*np.ones(L),beta=1/A_u**2,shape=L)
    sigma2_u = 
    pm.InverseGamma('sigma2_u',alpha=2*np.ones(L),beta=1/a_u,shape=L)
    a_eps = pm.InverseGamma('a_eps',alpha=2,beta=1/A_eps**2)
    sigma2_eps = pm.InverseGamma('sigma2_eps',alpha=2,beta=1/a_eps)
    a_R = pm.InverseGamma('a_R',alpha=2,beta=1/A_R**2,shape=q)
    Sigma_R_inv = pm.distributions.multivariate.Wishart('Sigma_R_inv',nu=v+q-1,V=1/(2*v)*T.nlinalg.AllocDiag(a_R))
    Sigma_R = T.nlinalg.MatrixInverse(Sigma_R_inv)
    u_R = pm.distributions.multivariate.MvNormal('u_R',np.zeros(m*q),T.slinalg.kron(np.eye(m),Sigma_R))
    u_G = pm.distributions.multivariate.MvNormal('u_G',np.zeros(L),T.nlinalg.AllocDiag(sigma2_u))
    beta = pm.Normal('beta',mu=0,sd=sigma_beta,shape=p)
    y = pm.Normal('y',mu = T.dot(X,beta)+T.dot(Z_R,u_R)+T.dot(Z_G,u_G),sd=sigma_eps,observed=Y) 
with smk:
    trace= pm.sample(10000)

然后报告错误，＆＃34; TypeError：object（）不带参数＆＃34;在这一行，

Sigma_R_inv = pm.distributions.multivariate.Wishart('Sigma_R_inv',nu=v+q-1,V=1/(2*v)*T.nlinalg.AllocDiag(a_R))

我想知道为什么，有人可以帮助我，谢谢！

Wisprt在pymc3中的分布

0 个答案: