# Sample posterior predictive error

**URL:** <https://discourse.pymc.io/t/sample-posterior-predictive-error/3590>\
**Category:** Questions\
**Created:** [July 18, 2019, 4:14pm UTC](https://discourse.pymc.io/t/sample-posterior-predictive-error/3590 "2019-07-18T16:14:52Z")\
**Posts on this page:** 4\
**Page:** 1

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**Author:** ![George](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/george/32/2012_2.png) [@George](https://discourse.pymc.io/u/George)\
**Post date:** [July 18, 2019, 4:14pm UTC](https://discourse.pymc.io/t/sample-posterior-predictive-error/3590/1 "2019-07-18T16:14:52Z")

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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.

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**Author:** ![bwengals](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/bwengals/32/1237_2.png) [@bwengals](https://discourse.pymc.io/u/bwengals)\
**Post date:** [July 18, 2019, 11:37pm UTC](https://discourse.pymc.io/t/sample-posterior-predictive-error/3590/2 "2019-07-18T23:37:48Z")

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The issue is that you are using `DensityDist` with a custom `logp`, but no `random` method was supplied. The error message refers to the fact that your custom distribution doesn’t know how to be sampled, since only a `logp` was given. All of the probability distributions in PyMC3 have a `logp` method, and also a `random` method. You’ll also have to supply `DensityDist` with a `random` arg, which can draw samples from your distribution given its parameter values.

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<div class="post-metadata">

**Author:** ![George](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/george/32/2012_2.png) [@George](https://discourse.pymc.io/u/George)\
**Post date:** [July 19, 2019, 2:11pm UTC](https://discourse.pymc.io/t/sample-posterior-predictive-error/3590/3 "2019-07-19T14:11:13Z")

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Thank you very much. Could you please also tell me where can I find some good examples on how to implement random() for a customized likelihood, especially for the weighted likelihood I use here. This likelihood a weighted sum of log likelihood of an Exponential and a Bernoulli distribution.

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<div class="post-metadata">

**Author:** ![bwengals](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/bwengals/32/1237_2.png) [@bwengals](https://discourse.pymc.io/u/bwengals)\
**Post date:** [October 8, 2019, 7:32pm UTC](https://discourse.pymc.io/t/sample-posterior-predictive-error/3590/4 "2019-10-08T19:32:48Z")

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Sorry for the slow response. Honestly I think the best place to look would be in PyMC3 source code. For example [here](https://github.com/pymc-devs/pymc3/blob/master/pymc3/distributions/continuous.py#L477). Take a look at the `draw_values` and `generate_samples` functions, but the actual RV sampling is done with scipy here.