PyMC @observed decorator computed log-probability cannot be cast to float 错误

Question

我在将自定义 @stochasitc 变量切换为 PyMC 中的 @observed 变量时遇到了一些问题。我有一个这样设置的随机变量：

def age_logp(age):
    if age < 0 or age > 110:
        return -np.inf
    elif 0 <= age < 20:
        prob = age_dist['0-20']
    elif 20 <= age < 40:
        prob = age_dist['20-40']
    elif 40 <= age < 60:
        prob = age_dist['40-60']
    elif 60 <= age < 80:
        prob = age_dist['60-80']
    elif age >= 80:
        prob = age_dist['80-inf']
    return np.log(prob)


@pymc.stochastic
def age(value=0):
    def logp(value):
        return age_logp(value)

如何将其切换为观察到的变量？到目前为止我已经试过了：

@pymc.observed
def age(value=np.array([12, 43, 28, 39, 87, 26])):
    return map(age_logp, value)

但是我得到了TypeError: age: computed log-probability [-1.639897119918809, -1.3394107752210402, -1.0876723486297752, -1.0876723486297752, -3.1235656450638758, -1.0876723486297752] cannot be cast to float

如何扩展 age 以获取观察值数组？

Answer 1

您已经正确地切换了一些东西，但是如果您想将多个观察值建模为独立的，您可以在联合对数似然中对它们求和：

@pymc.observed
def age(value=np.array([12, 43, 28, 39, 87, 26])):
    return sum(map(age_logp, value))

如果您愿意，可以通过其他方式组合它们。关键是 age 应该 return 一个标量值。

PyMC @observed decorator computed log-probability cannot be cast to float 错误

PyMC @observed decorator computed log-probability cannot be cast to float error

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