多维 PyMC3 观察

我的模型有一个 LogNormal RV，C，形状为 (W,D)。 W 中的每一行和 D 中的每一列都有一个正在拟合的参数。我试图将我的观察结果指定为 (W,D) 矩阵，但是，这会导致 theano 编译错误

raise Exception('Compilation failed (return status=%s): %s' %
Exception: ('The following error happened while compiling the node', Alloc(Elemwise{switch,no_inplace}.0, TensorConstant{10}, TensorConstant{10}), '\n', 'Compilation failed (return status=3): ', '[Alloc(<TensorType(float64, row)>, TensorConstant{10}, TensorConstant{10})]')

我使用的模型来自答案here https://stackoverflow.com/questions/60407008/summing-rvs-using-pymc3。该模型以及观察到的数据如下：

logloss = np.array([[6.85856503, 6.35784227, 7.15773548, 6.7262334 , 4.34380542,
        4.68213123, 4.20469262, 2.07944154, 1.38629436, 0.        ],
       [6.74405919, 6.57228254, 6.45833828, 5.43807931, 3.58351894,
        2.94443898, 3.25809654, 2.56494936, 1.60943791,        nan],
       [6.89060912, 7.11314211, 6.42810527, 6.90975328, 5.33271879,
        3.25809654, 3.61091791, 3.97029191,        nan,        nan],
       [7.41276402, 6.93537045, 6.18208491, 6.06610809, 5.70378247,
        6.04025471, 2.48490665,        nan,        nan,        nan],
       [6.83733281, 6.91572345, 6.53087763, 6.55961524, 3.58351894,
        4.81218436,        nan,        nan,        nan,        nan],
       [7.05789794, 7.12286666, 5.98393628, 5.28320373, 3.63758616,
               nan,        nan,        nan,        nan,        nan],
       [7.2984451 , 7.31455283, 6.8721281 , 6.64509097,        nan,
               nan,        nan,        nan,        nan,        nan],
       [7.12286666, 6.73340189, 6.26720055,        nan,        nan,
               nan,        nan,        nan,        nan,        nan],
       [7.18992217, 6.9902565 ,        nan,        nan,        nan,
               nan,        nan,        nan,        nan,        nan],
       [7.25347038,        nan,        nan,        nan,        nan,
               nan,        nan,        nan,        nan,        nan]])

logprem = np.array([8.66768002, 8.49862181, 8.60410456, 8.54966038, 8.55910259,
       8.56216656, 8.51559191, 8.60630237, 8.56140145, 8.50956416])

D = 10
W = 10

logprem = tt._shared(logprem).T.repeat(D, axis=1))

with pm.Model() as model:
    logelr = pm.Normal('logelr', -0.4, np.sqrt(10))

    # col vector
    alpha = pm.Normal("alpha", 0, sigma=np.sqrt(10), shape=(W-1,1))

    # row vector
    beta = pm.Normal("beta", 0, sigma=np.sqrt(10), shape=(1,D-1))

    # prepend zero and broadcast to (W,D)
    alpha_aug = tt.concatenate([tt.zeros((1,1)), alpha], axis=0).repeat(D, axis=1)

    # append zero and broadcast to (W,D)
    beta_aug = tt.concatenate([beta, tt.zeros((1,1))], axis=1).repeat(W, axis=0)

    # technically the indices will be reversed
    # e.g., a[0], a[9] here correspond to a_10, a_1 in the paper, resp.
    a = pm.Uniform('a', 0, 1, shape=D)

    # Note: the [::-1] sorts it in the order specified 
    # such that (sigma_0 > sigma_1 > ... )
    sigma = pm.Deterministic('sigma', tt.extra_ops.cumsum(a)[::-1].reshape((1,D)))

    # now everything here has shape (W,D) or is scalar (logelr)
    mu = logprem + logelr + alpha_aug + beta_aug

    # sigma will be broadcast automatically
    C = pm.Lognormal('C', mu=mu, sigma=sigma, shape=(W,D), observed=logloss)