File:Gaussianprocess.gif
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Gaussianprocess.gif (667 × 292 pixels, file size: 961 KB, MIME type: image/gif, looped, 49 frames, 4.9 s)
![]() | This is a file from the Wikimedia Commons. Information from its description page there is shown below. Commons is a freely licensed media file repository. You can help. |
Summary
DescriptionGaussianprocess.gif |
English: If you have some data and instead of fitting a model you want to estimate the reasonable shapes any smooth enough function compatible with them can take, you can look at the envelope of the random walks passing through the data. |
Date | |
Source | https://twitter.com/j_bertolotti/status/1249341481057468417 |
Author | Jacopo Bertolotti |
Permission (Reusing this file) |
https://twitter.com/j_bertolotti/status/1030470604418428929 |
Mathematica 12.0 code
data = {0 -> 0, 50 -> 3, 100 -> 10, 150 -> 5}; p = Predict[data, Method -> {"GaussianProcess", "CovarianceType" -> "SquaredExponential"}]; rw = Accumulate /@ RandomVariate[NormalDistribution[0, 0.5], {1000, 50}]; pos1 = Flatten[Position[rw[[All, -2]], # ] & /@ Select[rw[[All, -2]], Abs[# - 3] < 1 &] ]; pos2 = Flatten[Position[rw[[All, -2]], # ] & /@ Select[rw[[All, -2]], Abs[# - 7] < 1 &] ]; pos3 = Flatten[Position[rw[[All, -2]], # ] & /@ Select[rw[[All, -2]], Abs[# + 5] < 1 &] ]; n = Dimensions[pos2][[1]] p0 = Table[ Legended[Show[ListPlot[rw[[pos1]][[1 ;; n, 1 ;; Max[3, t] ]], Joined -> True, PlotStyle -> Directive[Black, Opacity[0.2]], DataRange -> {0.9, Max[3, t]} ] , ListPlot[List @@@ data, PlotStyle -> Directive[Purple, PointSize[0.03]] ] , PlotRange -> {{0, 160}, {-10, 20}} ] , Column[{LineLegend[{Purple}, {"Prediction"}], LineLegend[{Orange}, {"Credible Interval"}], PointLegend[{Purple}, {"Data"}]}] ] , {t, 0, 50, 5}]; p1 = Table[ Legended[Show[ListPlot[rw[[pos1]][[1 ;; n ]], Joined -> True, PlotStyle -> Directive[Black, Opacity[0.2]], DataRange -> {0.9, 50} ] , ListPlot[rw[[pos2]][[All, 1 ;; Max[3, t]]] + 3, Joined -> True, PlotStyle -> Directive[Black, Opacity[0.2]], DataRange -> {51, 51 + Max[3, t]} ] , ListPlot[List @@@ data, PlotStyle -> Directive[Purple, PointSize[0.03]] ], PlotRange -> {{0, 160}, {-10, 20}} ] , Column[{LineLegend[{Purple}, {"Prediction"}], LineLegend[{Orange}, {"Credible Interval"}], PointLegend[{Purple}, {"Data"}]}] ] , {t, 0, 50, 5}]; p2 = Table[ Legended[Show[ ListPlot[rw[[pos1]][[1 ;; n ]], Joined -> True, PlotStyle -> Directive[Black, Opacity[0.2]], DataRange -> {0.9, 50} ] , ListPlot[rw[[pos2]] + 3, Joined -> True, PlotStyle -> Directive[Black, Opacity[0.2]], DataRange -> {51, 100} ] , ListPlot[rw[[pos3]][[1 ;; n, 1 ;; Max[3, t]]] + 10, Joined -> True, PlotStyle -> Directive[Black, Opacity[0.2]], DataRange -> {101, 101 + Max[3, t]} ] , ListPlot[List @@@ data, PlotStyle -> Directive[Purple, PointSize[0.03]] ], PlotRange -> {{0, 160}, {-10, 20}} ] , Column[{LineLegend[{Purple}, {"Prediction"}], LineLegend[{Orange}, {"Credible Interval"}], PointLegend[{Purple}, {"Data"}]}] ] , {t, 0, 50, 5}]; p3 = Table[ Legended[Show[ ListPlot[rw[[pos1]][[1 ;; 35 ]], Joined -> True, PlotStyle -> Directive[Black, Opacity[0.2]], DataRange -> {0.9, 50} ] , ListPlot[rw[[pos2]] + 3, Joined -> True, PlotStyle -> Directive[Black, Opacity[0.2]], DataRange -> {51, 100} ] , ListPlot[rw[[pos3]][[1 ;; 35]] + 10, Joined -> True, PlotStyle -> Directive[Black, Opacity[0.2]], DataRange -> {101, 150} ] , Plot[{p[x], p[x] + StandardDeviation[p[x, "Distribution"]], p[x] - StandardDeviation[p[x, "Distribution"]]}, {x, -1, 150}, PlotStyle -> {Directive[Purple, Opacity[t]], Directive[Orange, Opacity[t]], Directive[Orange, Opacity[t]]}, Filling -> {2 -> {3}}, FillingStyle -> Opacity[t/3], Exclusions -> False, PerformanceGoal -> "Speed"] , ListPlot[List @@@ data, PlotStyle -> Directive[Purple, PointSize[0.03]] ], PlotRange -> {{0, 160}, {-10, 20}} ] , Column[{LineLegend[{Purple}, {"Prediction"}], LineLegend[{Orange}, {"Credible Interval"}], PointLegend[{Purple}, {"Data"}]}] ] , {t, 0, 1, 0.2}]; ListAnimate[p3] ListAnimate[Join[p0, p1, p2, p3, Table[p3[[-1]], {10}]] ]
Licensing
I, the copyright holder of this work, hereby publish it under the following license:
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This file is made available under the Creative Commons CC0 1.0 Universal Public Domain Dedication. |
The person who associated a work with this deed has dedicated the work to the public domain by waiving all of their rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.
http://creativecommons.org/publicdomain/zero/1.0/deed.enCC0Creative Commons Zero, Public Domain Dedicationfalsefalse |
Captions
Gaussian Process Regression as the envelope of random walks.
Items portrayed in this file
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some value
12 April 2020
image/gif
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Date/Time | Thumbnail | Dimensions | User | Comment | |
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current | 10:12, 13 April 2020 | ![]() | 667 × 292 (961 KB) | Berto | Uploaded own work with UploadWizard |
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