else:
names = [name]
data_arrays = [as_tensor(data, name) for data, name in zip(data_arrays,names)]
self.data = [d for d, missing in data_arrays]
self.missing_values = [missing for d, missing in data_arrays if missing is not None]
self.logp_elemwiset = distribution.logp(*data_arrays)
self.model = model
After Change
self.data = { name : as_tensor(data, name) for name, data in data.items()}
self.missing_values = [ data.missing_values for data in self.data.values() if data.missing_values is not None]
self.logp_elemwiset = distribution.logp(**self.data)
self.model = model
self.distribution = distribution
