Update app.py

app.py

@@ -15,7 +15,7 @@ beta = st.slider('Beta', 0.5, 5.0, 0.5)
 theta = np.linspace(0.01,0.99,100)
 #rc('font', size=20)
 # rc('text', usetex=True)
-fig, ax = plt.subplots()
+fig, ax = plt.subplots(figsize=(10,5))
 axs = ax.twinx()
 
 def Bernoulli(theta, N, h):
@@ -31,11 +31,11 @@ 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')
-axs.text(alpha/(alpha+beta)-0.1,1,'Prior mean')
+# 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((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')
+# 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')
 # ax.set_title(f"n_samples={int(N)}, n_heads={int(h)}");
 ax.tick_params(axis='y', colors='b')
 axs.tick_params(axis='y', colors='r')