遵循@ChrisFonnesbeck的建议,我写了一个有关增量优先更新的小型教程笔记本。在这里能找到它:
https://github.com/pymc- devs/pymc3/blob/master/docs/source/notebooks/updating_priors.ipynb
基本上,您需要将后验样本包装到自定义的Continuous类中,该类从它们中计算出KDE。以下代码可以做到这一点:
def from_posterior(param, samples):
class FromPosterior(Continuous):
def __init__(self, *args, **kwargs):
self.logp = logp
super(FromPosterior, self).__init__(*args, **kwargs)
smin, smax = np.min(samples), np.max(samples)
x = np.linspace(smin, smax, 100)
y = stats.gaussian_kde(samples)(x)
y0 = np.min(y) / 10 # what was never sampled should have a small probability but not 0
@as_op(itypes=[tt.dscalar], otypes=[tt.dscalar])
def logp(value):
# Interpolates from observed values
return np.array(np.log(np.interp(value, x, y, left=y0, right=y0)))
return FromPosterior(param, testval=np.median(samples))
然后,alpha
通过from_posterior
使用参数名称和来自上一次迭代的后验的跟踪样本来调用函数来定义模型参数的先验(例如):
alpha = from_posterior('alpha', trace['alpha'])