posterior and prior for head tf.py

py1_post = np.sum((res["theta"])*res["post"])
py0_post = 1.0 - py1_post
py0_post, py1_post

py1_prior = np.sum((res["theta"])*res["prior"]) 
py0_prior = 1 - py1_prior
py0_prior, py1_prior

#Plot posterior and prior
plt.figure(figsize=(16,12))
 
plt.subplot(2,2,1)
plt.stem(res["theta"],res["prior"])
plt.xlabel("theta")
plt.ylabel("probability")
plt.ylim([0,0.5])
plt.title("prior distribution for theta")
plt.subplot(2,2,2)
plt.stem(res["theta"],res["post"])
plt.ylim([0,0.5])
plt.xlabel("theta")
plt.ylabel("probability")
plt.title("posterior distribution for theta")
plt.savefig("test.pdf")
 
plt.subplot(2,2,3)
plt.stem([0,1],[py0_prior,py1_prior])
plt.xlabel("Y")
plt.ylabel("P(y)")
plt.ylim([0,1])
plt.title("prior predictive distribution")
plt.subplot(2,2,4)
plt.stem([0,1],[py0_post,py1_post])
plt.ylim([0,1])
plt.xlabel("Y")
plt.ylabel("P(y)")
plt.title("posterior predictive distribution")
 
 
#plt.show()
plt.savefig("ch07_b2a.pdf")
# from google.colab import files
# files.download('ch07_b2a') 
plt.show()