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of prior models in transmural electrophysiological imaging: A
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35(1), 229–243.
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Charlebois, C., Liu, Z., Bergquist, J. A., White, D., Rupp, L. C.,
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Neuromodulation, 14(6), 1659–1660.
https://doi.org/10.1016/j.brs.2021.10.226</unstructured_citation>
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imaging of myocardial infarction.</article_title>
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Interv</journal_title>
<issue>Pt 2</issue>
<volume>17</volume>
<doi>10.1007/978-3-319-10470-6_66</doi>
<cYear>2014</cYear>
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R., Gao, F., &amp; Wang, L. (2014). Variational bayesian
electrophysiological imaging of myocardial infarction. Med Image Comput
Comput Assist Interv, 17(Pt 2), 529–537.
https://doi.org/10.1007/978-3-319-10470-6_66</unstructured_citation>
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A Spectral Method Approach</volume_title>
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<cYear>2010</cYear>
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Stochastic Computations: A Spectral Method Approach. Princeton
University Press.
https://doi.org/10.1007/978-3-319-10470-6_66</unstructured_citation>
</citation>
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<article_title>Gaussian Processes in Machine
Learning</article_title>
<author>Rasmussen</author>
<journal_title>Advanced Lectures on Machine
Learning</journal_title>
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<isbn>978-3-540-28650-9</isbn>
<cYear>2004</cYear>
<unstructured_citation>Rasmussen, C. E. (2004). Gaussian
Processes in Machine Learning. In Advanced Lectures on Machine Learning
(pp. 63–71). Springer, Berlin, Heidelberg.
https://doi.org/10.1007/978-3-540-28650-9_4</unstructured_citation>
</citation>
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<article_title>Weighted Approximate Fekete Points: Sampling
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<author>Guo</author>
<journal_title>SIAM Journal on Scientific
Computing</journal_title>
<issue>1</issue>
<volume>40</volume>
<doi>10.1137/17M1140960</doi>
<issn>1064-8275</issn>
<cYear>2018</cYear>
<unstructured_citation>Guo, L., Narayan, A., Yan, L., &amp;
Zhou, T. (2018). Weighted Approximate Fekete Points: Sampling for
Least-Squares Polynomial Approximation. SIAM Journal on Scientific
Computing, 40(1), A366–A387.
https://doi.org/10.1137/17M1140960</unstructured_citation>
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<article_title>Optimal weighted least-squares
methods</article_title>
<author>Cohen</author>
<journal_title>SMAI Journal of Computational
Mathematics</journal_title>
<volume>3</volume>
<doi>10.5802/smai-jcm.24</doi>
<issn>2426-8399</issn>
<cYear>2017</cYear>
<unstructured_citation>Cohen, A., &amp; Migliorati, G.
(2017). Optimal weighted least-squares methods. SMAI Journal of
Computational Mathematics, 3, 181–203.
https://doi.org/10.5802/smai-jcm.24</unstructured_citation>
</citation>
<citation key="ACN:Nar2018">
<article_title>Computation of induced orthogonal polynomial
distributions</article_title>
<author>Narayan</author>
<journal_title>Electronic Transactions on Numerical
Analysis</journal_title>
<volume>50</volume>
<doi>10.1553/etna_vol50s71</doi>
<cYear>2018</cYear>
<unstructured_citation>Narayan, A. (2018). Computation of
induced orthogonal polynomial distributions. Electronic Transactions on
Numerical Analysis, 50, 71–97.
https://doi.org/10.1553/etna_vol50s71</unstructured_citation>
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