Added header

examples/var2.py

@@ -5,8 +5,8 @@
 Example: VAR(2) process
 =======================
 
-In this example, we demonstrate how to implement and perform Bayesian inference for a 
-Vector Autoregressive process of order 2 (VAR(2)). VAR models are widely used in 
+In this example, we demonstrate how to implement and perform Bayesian inference for a
+Vector Autoregressive process of order 2 (VAR(2)). VAR models are widely used in
 time series analysis, especially for capturing the dynamics between multiple variables.
 
 A VAR(2) process for a multivariate time series :math:`y_t` with :math:`K` variables is defined as:
@@ -15,8 +15,8 @@
 
     y_t = c + \Phi_1 y_{t-1} + \Phi_2 y_{t-2} + \epsilon_t
 
-Here, :math:`c` is a constant vector, :math:`\Phi_1` and :math:`\Phi_2` are coefficient matrices for lag 1 
-and lag 2, respectively, and :math:`\epsilon_t` is a Gaussian noise term with zero mean and a 
+Here, :math:`c` is a constant vector, :math:`\Phi_1` and :math:`\Phi_2` are coefficient matrices for lag 1
+and lag 2, respectively, and :math:`\epsilon_t` is a Gaussian noise term with zero mean and a
 covariance matrix :math:`\Sigma`.
 
 This example uses NumPyro's `scan` utility to efficiently model the temporal dependencies without