@PhDThesis{	  itlic:2016-011,
  author	= {Andreas Svensson},
  title		= {Learning Probabilistic Models of Dynamical Phenomena Using
		  Particle Filters},
  school	= {Department of Information Technology, Uppsala University},
  department	= {Division of Systems and Control},
  year		= {2016},
  number	= {2016-011},
  type		= {Licentiate thesis},
  month		= dec,
  day		= {16},
  abstract	= {Dynamical behavior can be seen in many real-life
		  phenomena, typically as a dependence over time. This thesis
		  studies and develops methods and probabilistic models for
		  statistical learning of such dynamical phenomena.
		  
		  A probabilistic model is a mathematical model expressed
		  using probability theory. Statistical learning amounts to
		  constructing such models, as well as adjusting them to data
		  recorded from real-life phenomena. The resulting models can
		  be used for, e.g., drawing conclusions about the phenomena
		  under study and making predictions.
		  
		  The methods in this thesis are primarily based on the
		  particle filter and its generalizations, sequential Monte
		  Carlo (SMC) and particle Markov chain Monte Carlo (PMCMC).
		  The model classes considered are nonlinear state-space
		  models and Gaussian processes.
		  
		  The following contributions are included. Starting with a
		  Gaussian-process state-space model, a general, flexible and
		  computationally feasible nonlinear state-space model is
		  derived in Paper I. In Paper II, a benchmark is performed
		  between the two alternative state-of-the-art methods SMCs
		  and PMCMC. Paper III considers PMCMC for solving the
		  state-space smoothing problem, in particular for an indoor
		  positioning application. In Paper IV, SMC is used for
		  marginalizing the hyperparameters in the Gaussian-process
		  state-space model, and Paper V is concerned with learning
		  of jump Markov linear state-space models. In addition, the
		  thesis also contains an introductory overview covering
		  statistical inference, state-space models, Gaussian
		  processes and some advanced Monte Carlo methods, as well as
		  two appendices summarizing some useful technical results.}
}