Update app.py

app.py

@@ -8,8 +8,8 @@ from matplotlib import rc
 st.subheader("Bayesian Coin Toss")
 st_col = st.columns(1)[0]
 
-N = st.slider('N_samples', min_value=2, max_value=20, value=10, step=1)
-h = st.slider('N_heads', min_value=2, max_value=20, value=8, step=1)
+N = st.slider('N_samples', min_value=2, max_value=20, value=5, step=1)
+h = st.slider('N_heads', min_value=2, max_value=20, value=4, 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)
 
@@ -31,10 +31,10 @@ 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[N_theta//4], Likelihood[N_theta//4], 'Likelihood', color='b',zorder=12)
+ax.text(theta[N_theta//4], Likelihood[N_theta//4], 'Likelihood', color='b',zorder=10)
 axs.text(theta[3*N_theta//4], scipy.stats.beta.pdf(theta, alpha,beta)[3*N_theta//4],'Prior',zorder=11)
 # axs.text(alpha/(alpha+beta)-0.1,1,'Prior mean')
-axs.text(theta[N_theta//2],scipy.stats.beta.pdf(theta[N_theta//2], h+alpha, N-h+beta),'Posterior',color='r',zorder=10)
+axs.text(theta[N_theta//2],scipy.stats.beta.pdf(theta[N_theta//2], h+alpha, N-h+beta),'Posterior',color='r',zorder=12)
 # 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')