Update app.py

app.py

@@ -12,7 +12,8 @@ h = st.slider('N_heads', min_value=2, max_value=20, value=8, step=1)
 alpha = st.slider('Alpha', min_value=0.5, max_value=5.0, value=2.0, step=0.1)
 beta = st.slider('Beta', min_value=0.5, max_value=5.0, value=2.0, step=0.1)
 
-theta = np.linspace(0.01,0.99,100)
+N_theta = 100
+theta = np.linspace(0.01,0.99,N_theta)
 #rc('font', size=20)
 # rc('text', usetex=True)
 fig, ax = plt.subplots(figsize=(10,4))
@@ -29,10 +30,10 @@ ax.set_xlabel('p(head)');
 # axs.text(h/N,2,'MLE', color='b')
 
 axs.plot(theta, [scipy.stats.beta.pdf(t, h+alpha, N-h+beta) for t in theta], color='r')
-ax.text(theta[0], Likelihood[0],'Likelihood', color='b')
-axs.text(theta[-5], scipy.stats.beta.pdf(theta, alpha,beta)[-5],'Prior')
+ax.text(theta[N_theta//4], Likelihood[N_theta//4],'Likelihood', color='b')
+axs.text(theta[3*N_theta//4], scipy.stats.beta.pdf(theta, alpha,beta)[3*N_theta//4],'Prior')
 # axs.text(alpha/(alpha+beta)-0.1,1,'Prior mean')
-axs.text(theta[0],scipy.stats.beta.pdf(theta[0], h+alpha, N-h+beta),'Posterior',color='r')
+axs.text(theta[N_theta//2],scipy.stats.beta.pdf(theta[N_theta//2], h+alpha, N-h+beta),'Posterior',color='r')
 # axs.text((h+alpha)/(N+alpha+beta)-0.1,3,'Post. Mean',color='r')
 # ax.vlines(alpha/(alpha+beta), *ax.get_ylim(), linestyle='--',label='Prior mean',color='k')
 # ax.vlines((h+alpha)/(N+alpha+beta), *ax.get_ylim(), linestyle='--',label='Post. Mean',color='r')