Update app.py

app.py

@@ -7,10 +7,12 @@ st.subheader("Gaussian Process Regression with Squared Exponential Kernel")
 st_col_text = st.columns(1)[0]
 
 st.markdown(r"""
-           Squared Exponential Kernel is formulated as $K(x, x') = \sigma_f^2 \exp \left[\frac{(x - x')^2}{\ell^2}\right]$ where, $\ell$ is lengthscale and $\sigma_f^2$ is variance.
+           Squared Exponential Kernel is formulated as $K(x, x') = \sigma_f^2 \exp \left[\frac{-(x - x')^2}{\ell^2}\right]$ where, $\ell$ is lengthscale and $\sigma_f^2$ is variance.
            $$
-           \text{Prior:} \mathbf{f} \sim \mathcal{N}(\boldsymbol{0}, K), \text{Likelihood} \sim \mathcal{N}(\mathbf{f}, \sigma_n^2) \text{ where $\sigma_n^2$ is noise variance or likelihood noise.}
+           \text{Prior:} \mathbf{f} \sim \mathcal{N}(\boldsymbol{0}, K) \\
+           \text{Likelihood} \sim \mathcal{N}(\mathbf{f}, \sigma_n^2)
            $$
+           where $\sigma_n^2$ is known as noise variance or likelihood noise.
     """)