Formulating statistical data assimilation with noisy measurements and model errors as a path integral over the path of a model state during a temporal observation window holds advantages over existing variational principles in data assimilation: two of these are in a framework where corrections to the variational methods can be evaluated and in an approach to identifying the minimum of the variational objective function. Examples from nonlinear models using in geophysics and neurobiology will be given as well an example from analysis of laboratory experiments on neurons in the songbird vocalization system.