@TechReport{	  it:2016-008,
  author	= {Andreas Svensson and Thomas B. Sch{\"o}n},
  title		= {Comparing Two Recent Particle Filter Implementations of
		  {B}ayesian System Identification},
  institution	= {Department of Information Technology, Uppsala University},
  department	= {Division of Systems and Control},
  year		= {2016},
  number	= {2016-008},
  month		= may,
  abstract	= {Bayesian system identification is a theoretically
		  well-founded and currently emerging area. We describe and
		  evaluate two recent state-of-the-art sample-based methods
		  for Bayesian parameter inference from the statistics
		  literature, particle Metropolis-Hastings (PMH) and SMC$^2$,
		  and apply them to a non-trivial real world system
		  identification problem with large uncertainty present. We
		  discuss their different properties from a user perspective,
		  and conclude that they show similar performance in
		  practice, while PMH is significantly easier to implement
		  than SMC$^2$.}
}