\begin{thebibliography}{19}
\providecommand{\natexlab}[1]{#1}
\providecommand{\url}[1]{\texttt{#1}}
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  \providecommand{\doi}[1]{doi: #1}\else
  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi

\bibitem[Chung et~al.(2014)Chung, Gulcehre, Cho, and
  Bengio]{chung2014empirical}
Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio.
\newblock Empirical evaluation of gated recurrent neural networks on sequence
  modeling.
\newblock In \emph{NIPS Workshop on Deep Learning}, 2014.

\bibitem[De~Iaco et~al.(2011)De~Iaco, Myers, and Posa]{de2011strict}
S~De~Iaco, DE~Myers, and D~Posa.
\newblock Strict positive definiteness of a product of covariance functions.
\newblock \emph{Communications in Statistics-Theory and Methods}, 2011.

\bibitem[Higham(1990)]{higham1990analysis}
Nicholas~J Higham.
\newblock \emph{Analysis of the Cholesky decomposition of a semi-definite
  matrix}.
\newblock Oxford University Press, 1990.

\bibitem[Li and Au(2019)]{li2019expectation}
Binbin Li and Siu-Kui Au.
\newblock An expectation-maximization algorithm for bayesian operational modal
  analysis with multiple (possibly close) modes.
\newblock \emph{Mechanical Systems and Signal Processing}, 2019.

\bibitem[Makridakis et~al.(2020)Makridakis, Spiliotis, and
  Assimakopoulos]{makridakis2020m4}
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos.
\newblock The m4 competition: 100,000 time series and 61 forecasting methods.
\newblock \emph{International Journal of Forecasting}, 2020.

\bibitem[Nielsen and Garcia(2009)]{nielsen2009statistical}
Frank Nielsen and Vincent Garcia.
\newblock Statistical exponential families: A digest with flash cards.
\newblock \emph{arXiv preprint arXiv:0911.4863}, 2009.

\bibitem[Oreshkin et~al.(2019)Oreshkin, Carpov, Chapados, and
  Bengio]{oreshkin2019n}
Boris~N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio.
\newblock N-beats: Neural basis expansion analysis for interpretable time
  series forecasting.
\newblock In \emph{ICLR}, 2019.

\bibitem[Papamakarios et~al.(2017)Papamakarios, Pavlakou, and
  Murray]{papamakarios2017masked}
George Papamakarios, Theo Pavlakou, and Iain Murray.
\newblock Masked autoregressive flow for density estimation.
\newblock In \emph{NeurIPS}, 2017.

\bibitem[Peharz et~al.(2016)Peharz, Gens, Pernkopf, and
  Domingos]{peharz2016latent}
Robert Peharz, Robert Gens, Franz Pernkopf, and Pedro Domingos.
\newblock On the latent variable interpretation in sum-product networks.
\newblock \emph{IEEE TPAMI}, 2016.

\bibitem[Peharz et~al.(2020)Peharz, Lang, Vergari, Stelzner, Molina, Trapp, den
  Broeck, Kersting, and Ghahramani]{PeharzLVS00BKG20}
Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina,
  Martin Trapp, Guy~Van den Broeck, Kristian Kersting, and Zoubin Ghahramani.
\newblock Einsum networks: Fast and scalable learning of tractable
  probabilistic circuits.
\newblock In \emph{ICML}, 2020.

\bibitem[Pourahmadi et~al.(2007)Pourahmadi, Daniels, and
  Park]{pourahmadi2007simultaneous}
Mohsen Pourahmadi, Michael~J Daniels, and Trevor Park.
\newblock Simultaneous modelling of the cholesky decomposition of several
  covariance matrices.
\newblock \emph{Journal of Multivariate Analysis}, 2007.

\bibitem[Salinas et~al.(2020)Salinas, Flunkert, Gasthaus, and
  Januschowski]{SALINAS20201181}
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski.
\newblock Deepar: Probabilistic forecasting with autoregressive recurrent
  networks.
\newblock \emph{International Journal of Forecasting}, 2020.

\bibitem[Sato(1999)]{sato1999fast}
Masa-aki Sato.
\newblock Fast learning of on-line em algorithm.
\newblock \emph{Rapport Technique, ATR Human Information Processing Research
  Laboratories}, 1999.

\bibitem[Shao et~al.(2020)Shao, Molina, Vergari, Stelzner, Peharz, Liebig, and
  Kersting]{shao2020cspn}
Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz,
  Thomas Liebig, and Kristian Kersting.
\newblock Conditional sum-product networks: Imposing structure on deep
  probabilistic architectures.
\newblock In \emph{PGM}, 2020.

\bibitem[Vaswani et~al.(2017)Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez,
  Kaiser, and Polosukhin]{vaswani2017attention}
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones,
  Aidan~N. Gomez, Lukasz Kaiser, and Illia Polosukhin.
\newblock Attention is all you need.
\newblock In \emph{NeurIPS}, 2017.

\bibitem[Whittle(1953)]{whittle1953analysis}
Peter Whittle.
\newblock The analysis of multiple stationary time series.
\newblock \emph{Journal of the Royal Statistical Society: Series B
  (Methodological)}, 1953.

\bibitem[Wolter et~al.(2020)Wolter, Gall, and Yao]{wolter2020sequence}
Moritz Wolter, J{\"u}rgen Gall, and Angela Yao.
\newblock Sequence prediction using spectral rnns.
\newblock In \emph{ICANN}, 2020.

\bibitem[Yang et~al.(2020)Yang, Ma, Li, Tsai, and
  Salakhutdinov]{yang2020complex}
Muqiao Yang, Martin~Q Ma, Dongyu Li, Yao-Hung~Hubert Tsai, and Ruslan
  Salakhutdinov.
\newblock Complex transformer: A framework for modeling complex-valued
  sequence.
\newblock In \emph{ICASSP}, 2020.

\bibitem[Yu et~al.(2021)Yu, Ventola, and Kersting]{yu2021icml_wspn}
Zhongjie Yu, Fabrizio Ventola, and Kristian Kersting.
\newblock Whittle networks: A deep likelihood model for time series.
\newblock In \emph{ICML}, 2021.

\end{thebibliography}
