# Bibliography

AM07

  Ryan Prescott Adams and David J C MacKay. Bayesian online changepoint detection. arxiv, October 2007. URL: http://arxiv.org/abs/0710.3742, arXiv:0710.3742.

AEspanaGGB+20

  Diego Agudelo-España, Sebastian Gomez-Gonzalez, Stefan Bauer, Bernhard Schölkopf, and Jan Peters. Bayesian online prediction of change points. In UAI, volume 124 of Proceedings of Machine Learning Research, 320–329. PMLR, 2020. URL: http://proceedings.mlr.press/v124/agudelo-espana20a.html.

BT12

  Matthew Botvinick and Marc Toussaint. Planning as inference. Trends Cogn. Sci., 16(10):485–488, October 2012. URL: https://pdfs.semanticscholar.org/2ba7/88647916f6206f7fcc137fe7866c58e6211e.pdf.

CP20

  Nicolas Chopin and Omiros Papaspiliopoulos. An Introduction to Sequential Monte Carlo. Springer, 1 edition, October 2020. URL: https://www.amazon.com/Introduction-Sequential-Monte-Springer-Statistics/dp/3030478440.

CWSchonN20

  Jarrad Courts, Adrian Wills, Thomas Schön, and Brett Ninness. Variational system identification for nonlinear State-Space models. arxiv, December 2020. URL: http://arxiv.org/abs/2012.05072, arXiv:2012.05072.

CWSchon21

  Jarrad Courts, Adrian G Wills, and Thomas B Schön. Gaussian variational state estimation for nonlinear State-Space models. IEEE Trans. Signal Process., 69:5979–5993, 2021. URL: https://arxiv.org/abs/2002.02620.

DEKM98

  R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, 1998.

Eve09

  Geir Evensen. Data Assimilation: The Ensemble Kalman Filter. Springer, 2nd ed. 2009 edition edition, August 2009. URL: https://www.amazon.com/Data-Assimilation-Ensemble-Kalman-Filter/dp/3642037100.

FL07

  P. Fearnhead and Z. Liu. Online inference for multiple changepoint problems. J. of Royal Stat. Soc. Series B, 69:589–605, 2007.

Fea04

  Paul Fearnhead. Exact bayesian curve fitting and signal segmentation. IEEE Trans. Signal Processing, 53:2160–2166, 2004. URL: http://www.maths.lancs.ac.uk/~fearnhea/software/ARPS.html.

Fea06

  Paul Fearnhead. Exact and efficient bayesian inference for multiple changepoint problems. Statistics and computing, 16:203–213, 2006.

FL11

  Paul Fearnhead and Zhen Liu. Efficient bayesian analysis of multiple changepoint models with dependence across segments. Statistics and Computing, 21(2):217–229, 2011. URL: https://eprints.lancs.ac.uk/id/eprint/26279/.

GarciaFernandezSSarkka17

  Ángel F García-Fernández, Lennart Svensson, and Simo Särkkä. Iterated posterior linearization smoother. IEEE Trans. Automat. Contr., 62(4):2056–2063, April 2017. URL: https://web.archive.org/web/20200506190022id_/https://research.chalmers.se/publication/249335/file/249335_Fulltext.pdf.

GarciaFernandezTSarkka19

  Ángel F García-Fernández, Filip Tronarp, and Simo Särkkä. Gaussian process classification using posterior linearization. IEEE Signal Process. Lett., 26(5):735–739, May 2019. URL: http://dx.doi.org/10.1109/LSP.2019.2906929.

GH96

  Z. Ghahramani and G. Hinton. Parameter estimation for linear dynamical systems. Technical Report CRG-TR-96-2, Dept. Comp. Sci., Univ. Toronto, 1996. URL: http://www.gatsby.ucl.ac.uk/~zoubin/papers/tr-96-2.ps.gz.

HSarkkaGarciaFernandez21

  Sakira Hassan, Simo Särkkä, and Ángel F García-Fernández. Temporal parallelization of inference in hidden markov models. IEEE Trans. Signal Processing, 69:4875–4887, February 2021. URL: http://arxiv.org/abs/2102.05743.

HKO23

  P. Hennig, H. Kersting, and M. Osborne. Probabilistic Numerics: Computation as Machine Learning. 2023. URL: https://www.probabilistic-numerics.org/textbooks/.

KGomezO12

  Hilbert J Kappen, Vicenç Gómez, and Manfred Opper. Optimal control as a graphical model inference problem. Mach. Learn., 87(2):159–182, May 2012. URL: https://doi.org/10.1007/s10994-012-5278-7.

Mur23

  K. P. Murphy. Probabilistic Machine Learning: Advanced Topics. MIT Press, 2023.

RTV12

  Konrad Rawlik, Marc Toussaint, and Sethu Vijayakumar. On stochastic optimal control and reinforcement learning by approximate inference. In Robotics: Science and Systems VIII. Robotics: Science and Systems Foundation, July 2012. URL: https://blogs.cuit.columbia.edu/zp2130/files/2019/03/On_Stochasitc_Optimal_Control_and_Reinforcement_Learning_by_Approximate_Inference.pdf.

RHFG17

  Michael Roth, Gustaf Hendeby, Carsten Fritsche, and Fredrik Gustafsson. The ensemble kalman filter: a signal processing perspective. EURASIP J. Adv. Signal Processing, 2017(1):56, August 2017. URL: https://doi.org/10.1186/s13634-017-0492-x.

Sar13

  Simo Sarkka. Bayesian Filtering and Smoothing. Cambridge University Press, 2013. URL: https://users.aalto.fi/~ssarkka/pub/cup_book_online_20131111.pdf.

SS19

  Simo Sarkka and Arno Solin. Applied stochastic differential equations. Cambridge University Press, 2019. URL: https://users.aalto.fi/~asolin/sde-book/sde-book.pdf.

SS20

  Simo Sarkka and Lennart Svensson. Levenberg-Marquardt and Line-Search extended kalman smoothers. In ICASSP. IEEE, May 2020. URL: https://users.aalto.fi/~ssarkka/pub/lm-eks-camera.pdf.

SarkkaGarciaFernandez21

  Simo Särkkä and Ángel F García-Fernández. Temporal parallelization of bayesian filters and smoothers. IEEE Trans. Automat. Contr., 2021. URL: http://arxiv.org/abs/1905.13002.

TGarciaFernandezSarkka18

  Filip Tronarp, Ángel F García-Fernández, and Simo Särkkä. Iterative filtering and smoothing in nonlinear and Non-Gaussian systems using conditional moments. IEEE Signal Process. Lett., 25(3):408–412, March 2018. URL: https://acris.aalto.fi/ws/portalfiles/portal/17669270/cm_parapub.pdf.

TKSarkkaH19

  Filip Tronarp, Hans Kersting, Simo Särkkä, and Philipp Hennig. Probabilistic solutions to ordinary differential equations as Non-Linear bayesian filtering: a new perspective. Stat. Comput., 29:1297–1315, 2019. URL: http://arxiv.org/abs/1810.03440.

WSarkkaS21

  William J Wilkinson, Simo Särkkä, and Arno Solin. Bayes-Newton methods for approximate bayesian inference with PSD guarantees. arxiv, November 2021. URL: http://arxiv.org/abs/2111.01721, arXiv:2111.01721.

previous

Optimal control