I tried to generate new datasets from inferred parameters using pm.sample_posterior_predictive(). But it does not work.

My codes are listed below:

```
#Load libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import pymc3 as pm
import theano.tensor as tt
#generate a dataset for parameter estimation (used as observations in model set up)
mu = 1/3.4
x1 = np.random.exponential(size=100, scale = mu)
p0 = 0.4
x2 = np.random.binomial(size=100, n=1, p= p0)
X = pd.DataFrame({'x1':x1,'x2':x2})
#define a customzied likelihood
N = X.shape[1] #number of dimension of the random vector
W = [1/N] * N #weight vector for each feature (each component in X)
#this can be ajusted by user, will be set an inuput of the
#simulation program
discrete_status = np.array([False,True,]) #tell sampler which components are discrete
def my_density(theta,W):
def logp(X):
def log_expo(lam,x):
return( tt.sum(tt.log(lam) - lam * x) )
def log_bernoulli(p,x):
return( tt.sum(1.* ( tt.switch( x, tt.log(p), tt.log(1 - p) ))) )
LL = np.array([
log_expo(tetha[0],X[:,0]),
log_bernoulli(tetha[1],X[:,1]),
])
return( np.dot(W,LL) )
return(logp)
#set up model
#hyperparameters for exponential distribution
hyper_expo_lower = 0.000001
hyper_expo_upper = 10
#hyperparameters for prior of bernoulli distribution
hyper_bern_lower = 0
hyper_bern_upper = 1
with pm.Model() as model:
##set prior for parameters
#for X0
r_0 = pm.Uniform('r_0',lower = hyper_expo_lower, upper = hyper_expo_upper)
#for X1
p_1 = pm.Uniform('p_1',lower = hyper_bern_lower, upper = hyper_bern_upper)
##wrap parameters into the vector tetha, which is used as one argument of weighted likelihood
tetha = np.array([
r_0, #for X0
p_1, #for X1
])
##set likelihood
joint_obs = pm.DensityDist('joint_obs',
my_density(tetha,#paramters
W,#weight
),
observed={'X' : X, #observations
}
)
#sampling paramters from posterior
with model:
step = pm.Metropolis()
step.discrete = discrete_status
step.any_discrete = True
step.all_discrete = False
samples = pm.sample(1000, step=step,cores=1,tune=500)
#look at results of parameter estimation
pm.summary(samples)
#posterior predictive sampling. Got Error here!!
ppc = pm.sample_posterior_predictive(samples, model=model, samples=100, size=10)
```

The last step gives error message:

```
ValueError: Distribution was not passed any random method Define a custom random method and pass it as kwarg random
```

Many thanks for your answer.