code: def log_likelihood(self, y, _const=math.log(2.0*math.pi), quiet=False):
          
          y = self._process_input(y)
          resid = y - self.mean.get_value(self._t)
          try:
              self._recompute()
          except solver.LinAlgError:
              if quiet:
                  return -np.inf
              raise
          if len(y.shape) > 1:
              raise ValueError("dimension mismatch")
          logdet = self.solver.log_determinant()
          if not np.isfinite(logdet):
              return -np.inf
          loglike = -0.5*(self.solver.dot_solve(resid)+logdet+len(y)*_const)
          if not np.isfinite(loglike):
              return -np.inf
          return loglike
docs: Compute the marginalized likelihood of the GP model
  
          The factorized matrix from the previous call to :func:`GP.compute` is
          used so ``compute`` must be called first.
  
          Args:
              y (array[n]): The observations at coordinates ``x`` from
                  :func:`GP.compute`.
              quiet (bool): If true, return ``-numpy.inf`` for non-positive
                  definite matrices instead of throwing an error.
  
          Returns:
              float: The marginalized likelihood of the GP model.
  
          Raises:
              ValueError: For mismatched dimensions.
              solver.LinAlgError: For non-positive definite matrices.