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<h1>Bibliography<a class="headerlink" href="#bibliography" title="Permalink to this headline">¶</a></h1>
<p id="id1"><dl class="citation">
<dt class="label" id="id23"><span class="brackets">AM07</span></dt>
<dd><p>Ryan Prescott Adams and David J C MacKay. Bayesian online changepoint detection. <em>arxiv</em>, October 2007. URL: <a class="reference external" href="http://arxiv.org/abs/0710.3742">http://arxiv.org/abs/0710.3742</a>, <a class="reference external" href="https://arxiv.org/abs/0710.3742">arXiv:0710.3742</a>.</p>
</dd>
<dt class="label" id="id22"><span class="brackets">AEspanaGGB+20</span></dt>
<dd><p>Diego Agudelo-España, Sebastian Gomez-Gonzalez, Stefan Bauer, Bernhard Schölkopf, and Jan Peters. Bayesian online prediction of change points. In <em>UAI</em>, volume 124 of Proceedings of Machine Learning Research, 320–329. PMLR, 2020. URL: <a class="reference external" href="http://proceedings.mlr.press/v124/agudelo-espana20a.html">http://proceedings.mlr.press/v124/agudelo-espana20a.html</a>.</p>
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<dt class="label" id="id19"><span class="brackets">BT12</span></dt>
<dd><p>Matthew Botvinick and Marc Toussaint. Planning as inference. <em>Trends Cogn. Sci.</em>, 16(10):485–488, October 2012. URL: <a class="reference external" href="https://pdfs.semanticscholar.org/2ba7/88647916f6206f7fcc137fe7866c58e6211e.pdf">https://pdfs.semanticscholar.org/2ba7/88647916f6206f7fcc137fe7866c58e6211e.pdf</a>.</p>
</dd>
<dt class="label" id="id34"><span class="brackets">CMR05</span></dt>
<dd><p>O. Cappe, E. Moulines, and T. Ryden. <em>Inference in Hidden Markov Models</em>. Springer, 2005.</p>
</dd>
<dt class="label" id="id31"><span class="brackets">CP20</span></dt>
<dd><p>Nicolas Chopin and Omiros Papaspiliopoulos. <em>An Introduction to Sequential Monte Carlo</em>. Springer, 1 edition, October 2020. URL: <a class="reference external" href="https://www.amazon.com/Introduction-Sequential-Monte-Springer-Statistics/dp/3030478440">https://www.amazon.com/Introduction-Sequential-Monte-Springer-Statistics/dp/3030478440</a>.</p>
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<dt class="label" id="id12"><span class="brackets">CWSchonN20</span></dt>
<dd><p>Jarrad Courts, Adrian Wills, Thomas Schön, and Brett Ninness. Variational system identification for nonlinear State-Space models. <em>arxiv</em>, December 2020. URL: <a class="reference external" href="http://arxiv.org/abs/2012.05072">http://arxiv.org/abs/2012.05072</a>, <a class="reference external" href="https://arxiv.org/abs/2012.05072">arXiv:2012.05072</a>.</p>
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<dt class="label" id="id13"><span class="brackets">CWSchon21</span></dt>
<dd><p>Jarrad Courts, Adrian G Wills, and Thomas B Schön. Gaussian variational state estimation for nonlinear State-Space models. <em>IEEE Trans. Signal Process.</em>, 69:5979–5993, 2021. URL: <a class="reference external" href="https://arxiv.org/abs/2002.02620">https://arxiv.org/abs/2002.02620</a>.</p>
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<dt class="label" id="id35"><span class="brackets">Dev85</span></dt>
<dd><p>Pierre A Devijver. Baum's forward-backward algorithm revisited. <em>Pattern Recognition Letters</em>, 3(6):369–373, 1985.</p>
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<dt class="label" id="id3"><span class="brackets">DEKM98</span></dt>
<dd><p>R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. <em>Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids</em>. Cambridge University Press, 1998.</p>
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<dt class="label" id="id4"><span class="brackets">Eve09</span></dt>
<dd><p>Geir Evensen. <em>Data Assimilation: The Ensemble Kalman Filter</em>. Springer, 2nd ed. 2009 edition edition, August 2009. URL: <a class="reference external" href="https://www.amazon.com/Data-Assimilation-Ensemble-Kalman-Filter/dp/3642037100">https://www.amazon.com/Data-Assimilation-Ensemble-Kalman-Filter/dp/3642037100</a>.</p>
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<dt class="label" id="id26"><span class="brackets">FL07</span></dt>
<dd><p>P. Fearnhead and Z. Liu. Online inference for multiple changepoint problems. <em>J. of Royal Stat. Soc. Series B</em>, 69:589–605, 2007.</p>
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<dt class="label" id="id24"><span class="brackets">Fea04</span></dt>
<dd><p>Paul Fearnhead. Exact bayesian curve fitting and signal segmentation. <em>IEEE Trans. Signal Processing</em>, 53:2160–2166, 2004. URL: <a class="reference external" href="http://www.maths.lancs.ac.uk/~fearnhea/software/ARPS.html">http://www.maths.lancs.ac.uk/~fearnhea/software/ARPS.html</a>.</p>
</dd>
<dt class="label" id="id25"><span class="brackets">Fea06</span></dt>
<dd><p>Paul Fearnhead. Exact and efficient bayesian inference for multiple changepoint problems. <em>Statistics and computing</em>, 16:203–213, 2006.</p>
</dd>
<dt class="label" id="id27"><span class="brackets">FL11</span></dt>
<dd><p>Paul Fearnhead and Zhen Liu. Efficient bayesian analysis of multiple changepoint models with dependence across segments. <em>Statistics and Computing</em>, 21(2):217–229, 2011. URL: <a class="reference external" href="https://eprints.lancs.ac.uk/id/eprint/26279/">https://eprints.lancs.ac.uk/id/eprint/26279/</a>.</p>
</dd>
<dt class="label" id="id33"><span class="brackets">Fra08</span></dt>
<dd><p>A. Fraser. <em>Hidden Markov Models and Dynamical Systems</em>. SIAM Press, 2008.</p>
</dd>
<dt class="label" id="id9"><span class="brackets">GarciaFernandezSSarkka17</span></dt>
<dd><p>Ángel F García-Fernández, Lennart Svensson, and Simo Särkkä. Iterated posterior linearization smoother. <em>IEEE Trans. Automat. Contr.</em>, 62(4):2056–2063, April 2017. URL: <a class="reference external" href="https://web.archive.org/web/20200506190022id_/https://research.chalmers.se/publication/249335/file/249335_Fulltext.pdf">https://web.archive.org/web/20200506190022id_/https://research.chalmers.se/publication/249335/file/249335_Fulltext.pdf</a>.</p>
</dd>
<dt class="label" id="id11"><span class="brackets">GarciaFernandezTSarkka19</span></dt>
<dd><p>Ángel F García-Fernández, Filip Tronarp, and Simo Särkkä. Gaussian process classification using posterior linearization. <em>IEEE Signal Process. Lett.</em>, 26(5):735–739, May 2019. URL: <a class="reference external" href="http://dx.doi.org/10.1109/LSP.2019.2906929">http://dx.doi.org/10.1109/LSP.2019.2906929</a>.</p>
</dd>
<dt class="label" id="id7"><span class="brackets">HSarkkaGarciaFernandez21</span></dt>
<dd><p>Sakira Hassan, Simo Särkkä, and Ángel F García-Fernández. Temporal parallelization of inference in hidden markov models. <em>IEEE Trans. Signal Processing</em>, 69:4875–4887, February 2021. URL: <a class="reference external" href="http://arxiv.org/abs/2102.05743">http://arxiv.org/abs/2102.05743</a>.</p>
</dd>
<dt class="label" id="id30"><span class="brackets">HKO23</span></dt>
<dd><p>P. Hennig, H. Kersting, and M. Osborne. <em>Probabilistic Numerics: Computation as Machine Learning</em>. 2023. URL: <a class="reference external" href="https://www.probabilistic-numerics.org/textbooks/">https://www.probabilistic-numerics.org/textbooks/</a>.</p>
</dd>
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<dd><p>Hilbert J Kappen, Vicenç Gómez, and Manfred Opper. Optimal control as a graphical model inference problem. <em>Mach. Learn.</em>, 87(2):159–182, May 2012. URL: <a class="reference external" href="https://doi.org/10.1007/s10994-012-5278-7">https://doi.org/10.1007/s10994-012-5278-7</a>.</p>
</dd>
<dt class="label" id="id17"><span class="brackets">Mur23</span></dt>
<dd><p>K. P. Murphy. <em>Probabilistic Machine Learning: Advanced Topics</em>. MIT Press, 2023.</p>
</dd>
<dt class="label" id="id32"><span class="brackets">Rab89</span></dt>
<dd><p>L. R. Rabiner. A tutorial on Hidden Markov Models and selected applications in speech recognition. <em>Proc. of the IEEE</em>, 77(2):257–286, 1989.</p>
</dd>
<dt class="label" id="id21"><span class="brackets">RTV12</span></dt>
<dd><p>Konrad Rawlik, Marc Toussaint, and Sethu Vijayakumar. On stochastic optimal control and reinforcement learning by approximate inference. In <em>Robotics: Science and Systems VIII</em>. Robotics: Science and Systems Foundation, July 2012. URL: <a class="reference external" href="https://blogs.cuit.columbia.edu/zp2130/files/2019/03/On_Stochasitc_Optimal_Control_and_Reinforcement_Learning_by_Approximate_Inference.pdf">https://blogs.cuit.columbia.edu/zp2130/files/2019/03/On_Stochasitc_Optimal_Control_and_Reinforcement_Learning_by_Approximate_Inference.pdf</a>.</p>
</dd>
<dt class="label" id="id5"><span class="brackets">RHFG17</span></dt>
<dd><p>Michael Roth, Gustaf Hendeby, Carsten Fritsche, and Fredrik Gustafsson. The ensemble kalman filter: a signal processing perspective. <em>EURASIP J. Adv. Signal Processing</em>, 2017(1):56, August 2017. URL: <a class="reference external" href="https://doi.org/10.1186/s13634-017-0492-x">https://doi.org/10.1186/s13634-017-0492-x</a>.</p>
</dd>
<dt class="label" id="id18"><span class="brackets">Sar13</span></dt>
<dd><p>Simo Sarkka. <em>Bayesian Filtering and Smoothing</em>. Cambridge University Press, 2013. URL: <a class="reference external" href="https://users.aalto.fi/~ssarkka/pub/cup_book_online_20131111.pdf">https://users.aalto.fi/~ssarkka/pub/cup_book_online_20131111.pdf</a>.</p>
</dd>
<dt class="label" id="id29"><span class="brackets">SS19</span></dt>
<dd><p>Simo Sarkka and Arno Solin. <em>Applied stochastic differential equations</em>. Cambridge University Press, 2019. URL: <a class="reference external" href="https://users.aalto.fi/~asolin/sde-book/sde-book.pdf">https://users.aalto.fi/~asolin/sde-book/sde-book.pdf</a>.</p>
</dd>
<dt class="label" id="id6"><span class="brackets">SS20</span></dt>
<dd><p>Simo Sarkka and Lennart Svensson. Levenberg-Marquardt and Line-Search extended kalman smoothers. In <em>ICASSP</em>. IEEE, May 2020. URL: <a class="reference external" href="https://users.aalto.fi/~ssarkka/pub/lm-eks-camera.pdf">https://users.aalto.fi/~ssarkka/pub/lm-eks-camera.pdf</a>.</p>
</dd>
<dt class="label" id="id14"><span class="brackets">SarkkaGarciaFernandez21</span></dt>
<dd><p>Simo Särkkä and Ángel F García-Fernández. Temporal parallelization of bayesian filters and smoothers. <em>IEEE Trans. Automat. Contr.</em>, 2021. URL: <a class="reference external" href="http://arxiv.org/abs/1905.13002">http://arxiv.org/abs/1905.13002</a>.</p>
</dd>
<dt class="label" id="id10"><span class="brackets">TGarciaFernandezSarkka18</span></dt>
<dd><p>Filip Tronarp, Ángel F García-Fernández, and Simo Särkkä. Iterative filtering and smoothing in nonlinear and Non-Gaussian systems using conditional moments. <em>IEEE Signal Process. Lett.</em>, 25(3):408–412, March 2018. URL: <a class="reference external" href="https://acris.aalto.fi/ws/portalfiles/portal/17669270/cm_parapub.pdf">https://acris.aalto.fi/ws/portalfiles/portal/17669270/cm_parapub.pdf</a>.</p>
</dd>
<dt class="label" id="id28"><span class="brackets">TKSarkkaH19</span></dt>
<dd><p>Filip Tronarp, Hans Kersting, Simo Särkkä, and Philipp Hennig. Probabilistic solutions to ordinary differential equations as Non-Linear bayesian filtering: a new perspective. <em>Stat. Comput.</em>, 29:1297–1315, 2019. URL: <a class="reference external" href="http://arxiv.org/abs/1810.03440">http://arxiv.org/abs/1810.03440</a>.</p>
</dd>
<dt class="label" id="id15"><span class="brackets">WSarkkaS21</span></dt>
<dd><p>William J Wilkinson, Simo Särkkä, and Arno Solin. Bayes-Newton methods for approximate bayesian inference with PSD guarantees. <em>arxiv</em>, November 2021. URL: <a class="reference external" href="http://arxiv.org/abs/2111.01721">http://arxiv.org/abs/2111.01721</a>, <a class="reference external" href="https://arxiv.org/abs/2111.01721">arXiv:2111.01721</a>.</p>
</dd>
</dl>
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