tensorflow_probability/python/distributions/linear_gaussian_ssm.py
def _propagate_cov(cov, linop, dist):
  """Propagate covariance through linear Gaussian transformation."""
  # For linop A and input cov P, returns `A P A' + dist.cov()`
  return linop.matmul(linop.matmul(cov), adjoint_arg=True) + dist.covariance()
def _propagate_cov(cov, linop, dist):
  # For linop A and input cov P, returns `A P A' + dist.cov()`
  return linop.matmul(linop.matmul(cov), adjoint_arg=True) + dist.covariance()
def _propagate_cov ( cov , linop , dist ) : # For linop A and input cov P, returns `A P A' + dist.cov()` return linop . matmul ( linop . matmul ( cov ) , adjoint_arg = True ) + dist . covariance ( )
Propagate covariance through linear Gaussian transformation.
Propagate covariance through linear Gaussian transformation .