refs.bib

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@Book{Barber2012,
  author    = {David Barber},
  publisher = {{Cambridge University Press}},
  title     = {Bayesian Reasoning and Machine Learning},
  year      = {2012},
  owner     = {mgutmann},
  timestamp = {2022.06.02},
  url       = {http://www.cs.ucl.ac.uk/staff/d.barber/brml/},
}

@Inproceedings{Frey2003,
  author    = {Brendan J. Frey},
  booktitle = {Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI)},
  title     = {Extending Factor Graphs so as to Unify Directed and Undirected Graphical Models},
  year      = {2003},
  owner     = {mgutmann},
  timestamp = {2022.06.02},
  url       = {https://arxiv.org/abs/1212.2486},
}

@Book{Chopin2020,
  author    = {Chopin, Nicolas and Papaspiliopoulos, Omiros},
  publisher = {Springer},
  title     = {An introduction to Sequential Monte Carlo},
  year      = {2020},
  abstract  = {This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the sequential analysis of data in such diverse fields as signal processing, epidemiology, machine learning, population ecology, quantitative finance, and robotics. The coverage is comprehensive, ranging from the underlying theory to computational implementation, methodology, and diverse applications in various areas of science. This is achieved by describing SMC algorithms as particular cases of a general framework, which involves concepts such as Feynman-Kac distributions, and tools such as importance sampling and resampling. This general framework is used consistently throughout the book. Extensive coverage is provided on sequential learning (filtering, smoothing) of state-space (hidden Markov) models, as this remains an important application of SMC methods. More recent applications, such as parameter estimation of these models (through e.g. particle Markov chain Monte Carlo techniques) and the simulation of challenging probability distributions (in e.g. Bayesian inference or rare-event problems), are also discussed. The book may be used either as a graduate text on Sequential Monte Carlo methods and state-space modeling, or as a general reference work on the area. Each chapter includes a set of exercises for self-study, a comprehensive bibliography, and a “Python corner,” which discusses the practical implementation of the methods covered. In addition, the book comes with an open source Python library, which implements all the algorithms described in the book, and contains all the programs that were used to perform the numerical experiments.},
  keywords  = {Monte Carlo method},
  owner     = {mgutmann},
  timestamp = {2022.06.03},
  url       = {https://link.springer.com/book/10.1007/978-3-030-47845-2},
}

@Article{Grewal2010,
  author  = {Grewal, Mohinder S. and Andrews, Angus P.},
  journal = {IEEE Control Systems Magazine},
  title   = {Applications of Kalman Filtering in Aerospace 1960 to the Present [Historical Perspectives]},
  year    = {2010},
  number  = {3},
  pages   = {69-78},
  volume  = {30},
  url     = {https://ieeexplore.ieee.org/document/5466132},
}

@Book{Bishop2006,
  author    = {Christopher M. Bishop},
  publisher = {Springer},
  title     = {Pattern Recognition and Machine Learning},
  year      = {2006},
  owner     = {mgutmann},
  timestamp = {2022.06.03},
  url       = {https://link.springer.com/book/9780387310732},
}

@Article{Hyvarinen1999,
  author  = {Hyv\"arinen, Aapo},
  journal = {IEEE Transactions on Neural Networks},
  title   = {Fast and robust fixed-point algorithms for independent component analysis},
  year    = {1999},
  number  = {3},
  pages   = {626-634},
  volume  = {10},
  url     = {https://ieeexplore.ieee.org/document/761722},
}

@Book{Hyvarinen2001,
  author    = {Aapo Hyv\"arinen and Erkki Oja and Juha Karhunen},
  publisher = {John Wiley \& Sons},
  title     = {Independent Component Analysis},
  year      = {2001},
  owner     = {mgutmann},
  timestamp = {2022.06.03},
  url       = {https://www.cs.helsinki.fi/u/ahyvarin/papers/bookfinal_ICA.pdf},
}

@Article{Hyvarinen2005c,
  author    = {Hyv{\"a}rinen, Aapo},
  journal   = {Journal of Machine Learning Research},
  title     = {{E}stimation of non-normalized statistical models using score matching},
  year      = {2005},
  pages     = {695--709},
  volume    = {6},
  owner     = {gutmann},
  timestamp = {2009.01.27},
  url       = {http://jmlr.org/papers/volume6/hyvarinen05a/hyvarinen05a.pdf},
}

@Book{Robert2010,
  author    = {Christian Robert and George Casella},
  publisher = {Springer},
  title     = {Introducing Monte Carlo Methods with R},
  year      = {2010},
  owner     = {mgutmann},
  timestamp = {2022.06.03},
  url       = {https://link.springer.com/book/10.1007/978-1-4419-1576-4},
}

@Book{Owen2013,
  author = {Art B. Owen},
  title  = {Monte Carlo theory, methods and examples},
  year   = {2013},
  url    = {https://artowen.su.domains/mc/},
}

@Book{Nocedal1999,
  author    = {Nocedal, Jorge and Wright, Stephen J.},
  publisher = {Springer},
  title     = {{N}umerical {O}ptimization},
  year      = {1999},
  groups    = {Beenish Master Thesis},
  owner     = {gutmann},
  timestamp = {2011.09.04},
}

@Book{Rudin1976,
  author    = {Walter Rudin},
  publisher = {McGraw Hill},
  title     = {Principles of Mathematical Analysis},
  year      = {1976},
  edition   = {3rd edition},
  owner     = {mgutmann},
  timestamp = {2022.06.09},
}

@Comment{jabref-meta: databaseType:bibtex;}