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Juarez et al 2021b - Revision history
2026-04-25T10:43:55Z
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Move page script: Move page script moved page Draft Content 652313104 to Juarez et al 2021b
2021-07-01T07:41:25Z
<p>Move page script moved page <a href="/public/Draft_Content_652313104" class="mw-redirect" title="Draft Content 652313104">Draft Content 652313104</a> to <a href="/public/Juarez_et_al_2021b" title="Juarez et al 2021b">Juarez et al 2021b</a></p>
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<td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 07:41, 1 July 2021</td>
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Scipediacontent at 07:34, 1 July 2021
2021-07-01T07:34:37Z
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 07:34, 1 July 2021</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>== Document ==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>== Document ==</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"><pdf>Media:Draft_Content_652313104_6637_P_IDC6_437.pdf</pdf></ins></div></td></tr>
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Scipediacontent at 07:30, 1 July 2021
2021-07-01T07:30:27Z
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 07:30, 1 July 2021</td>
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<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>== <del class="diffchange diffchange-inline">Abstract </del>==</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>== <ins class="diffchange diffchange-inline">Summary </ins>==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>We consider a statistical inversion computational model with Gaussian distributions for the numerical solution of the Cauchy problem for the Laplace equation. The a priori model is built up from Gaussian Markov random fields. Different precision matrices for the Cauchy problem are introduced. We take advantage of the relationship between the a priori distribution and traditional Tikhonov regularization to propose different models where smooth and non-smooth regularization is possible. A low range analysis allow us to estimate the optimal dimension of data and its relation to the the unknown.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>We consider a statistical inversion computational model with Gaussian distributions for the numerical solution of the Cauchy problem for the Laplace equation. The a priori model is built up from Gaussian Markov random fields. Different precision matrices for the Cauchy problem are introduced. We take advantage of the relationship between the a priori distribution and traditional Tikhonov regularization to propose different models where smooth and non-smooth regularization is possible. A low range analysis allow us to estimate the optimal dimension of data and its relation to the the unknown.</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>                                                  </div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>                                                  </div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>== <del class="diffchange diffchange-inline">Document </del>==</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>== <ins class="diffchange diffchange-inline">Abstract </ins>==</div></td></tr>
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Scipediacontent
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Scipediacontent: Created page with "== Abstract == We consider a statistical inversion computational model with Gaussian distributions for the numerical solution of the Cauchy problem for the Laplace equation...."
2021-07-01T07:28:14Z
<p>Created page with "== Abstract == We consider a statistical inversion computational model with Gaussian distributions for the numerical solution of the Cauchy problem for the Laplace equation...."</p>
<p><b>New page</b></p><div>== Abstract ==<br />
<br />
We consider a statistical inversion computational model with Gaussian distributions for the numerical solution of the Cauchy problem for the Laplace equation. The a priori model is built up from Gaussian Markov random fields. Different precision matrices for the Cauchy problem are introduced. We take advantage of the relationship between the a priori distribution and traditional Tikhonov regularization to propose different models where smooth and non-smooth regularization is possible. A low range analysis allow us to estimate the optimal dimension of data and its relation to the the unknown.<br />
<br />
== Video ==<br />
{{#evt:service=cloudfront|id=259149|alignment=center|filename=437.mp4}}<br />
<br />
== Document ==<br />
<pdf>Media:Draft_Content_652313104A_IDC6_437.pdf</pdf></div>
Scipediacontent