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def sample_posterior_predictive(trace, | |
samples: Optional[int]=None, | |
model: Optional[Model]=None, | |
vars: Optional[TIterable[Tensor]]=None, | |
var_names: Optional[List[str]]=None, | |
size: Optional[int]=None, | |
keep_size: Optional[bool]=False, | |
random_seed=None, | |
progressbar: bool=True) -> Dict[str, np.ndarray]: | |
"""Generate posterior predictive samples from a model given a trace. |
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from __future__ import print_function, unicode_literals, division, absolute_import | |
import theano as tt | |
import pymc3 as pm | |
from scipy import stats | |
import sys, traceback | |
class Foo(pm.Continuous): | |
def __init__(self, a, b): | |
self.a = tt.tensor.as_tensor_variable(a) | |
self.b = tt.tensor.as_tensor_variable(b) |