Update app.py

app.py

@@ -49,8 +49,8 @@ def run_linear_regression(X, y, alpha_prior, beta_prior, sigma_prior):
 # User Input
 st.write("# Bayesian Linear Regression")
 
-st.latex(r'''\textbf{Prior:} \hspace{0.5 cm} p(\alpha) \distas N(\mu_0, \Sigma_0),\hspace{0.2 cm} p(\beta) = N(\mu_1, \Sigma_1),\hspace{0.2 cm} p(\sigma) = N(\mu_2, \Sigma_2)''')
-st.latex(r'''\textbf{Likelihood:} \hspace{0.5 cm} p(y | X, \beta,\alpha, \sigma) \distas N(X\beta + \alpha, \sigma)''')
+st.latex(r'''\textbf{Prior:} \hspace{0.5 cm} p(\alpha) = N(\mu_0, \Sigma_0),\hspace{0.2 cm} p(\beta) = N(\mu_1, \Sigma_1),\hspace{0.2 cm} p(\sigma) = N(\mu_2, \Sigma_2)''')
+st.latex(r'''\textbf{Likelihood:} \hspace{0.5 cm} p(y | X, \beta,\alpha, \sigma) = N(X\beta + \alpha, \sigma)''')
 st.latex(r'''\textbf{Posterior:} \hspace{0.5 cm} p(\alpha | X, y) \propto p(y | X, \beta, \sigma) p(\alpha),\hspace{0.2 cm} p(\beta | X, y) \propto p(y | X, \alpha, \sigma) p(\beta),\hspace{0.2 cm} p(\sigma | X, y) \propto p(y | X, \beta, \alpha) p(\sigma)''')
 
 alpha_prior_option = st.selectbox("Choose an option for alpha prior:", ["Normal", "Laplace", "Cauchy"])