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#gaussian

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IT News<p>2025 One Hertz Challenge: Metronalmost is Gunning for Last Place - We’ve just begun to receive entries to the One Hertz Challenge, but we already hav... - <a href="https://hackaday.com/2025/07/15/2025-one-hertz-challenge-metronalmost-is-gunning-for-last-place/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">hackaday.com/2025/07/15/2025-o</span><span class="invisible">ne-hertz-challenge-metronalmost-is-gunning-for-last-place/</span></a> <a href="https://schleuss.online/tags/clockhacks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>clockhacks</span></a> <a href="https://schleuss.online/tags/metronome" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>metronome</span></a> <a href="https://schleuss.online/tags/contests" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>contests</span></a> <a href="https://schleuss.online/tags/gaussian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>gaussian</span></a> <a href="https://schleuss.online/tags/esp" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>esp</span></a>-12e</p>
R.L. Dane :Debian: :OpenBSD: 🍵 :MiraLovesYou:<p>Ok, this is <em><strong>amazing</strong></em>.</p><p><a href="https://inv.nadeko.net/watch?v=xDLxFGXuPEc" rel="nofollow noopener" target="_blank">https://inv.nadeko.net/watch?v=xDLxFGXuPEc</a></p><p>YT - Captain Disillusion - CD / Blur</p><p><a href="https://polymaths.social/tags/blur" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Blur</span></a> <a href="https://polymaths.social/tags/deblur" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DeBlur</span></a> <a href="https://polymaths.social/tags/convolution" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Convolution</span></a> <a href="https://polymaths.social/tags/gaussian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Gaussian</span></a> <a href="https://polymaths.social/tags/deconvolution" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DeConvolution</span></a> <a href="https://polymaths.social/tags/fourier" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Fourier</span></a></p>
okanogen VerminEnemyFromWithin<p>Back to topic.<br>Modelling has changed and improved a lot. Mostly due to more powerful computers allowing more brute force analysis. The math tools really haven't changed that much. Early commercial modelling included programs like <a href="https://mastodon.social/tags/Surfer" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Surfer</span></a> decades ago and used <a href="https://mastodon.social/tags/Kriging" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Kriging</span></a>, (<a href="https://mastodon.social/tags/Gaussian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Gaussian</span></a> peocess regression) of geostatistical data. Most current geospatial modelling uses this technique, often enhanced with <a href="https://mastodon.social/tags/BayesianDataAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianDataAnalysis</span></a> (read the book by that name). So now the table is mostly set. 5/</p>
Dr Mircea Zloteanu ☀️ 🌊🌴<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statstab</span></a> #204 GLMMadaptive: Generalized Linear Mixed Models using Adaptive Gaussian Quadrature</p><p>Thoughts: No clue what this package is does, but seems useful. Maybe someone can explain some use cases.</p><p><a href="https://mastodon.social/tags/glmm" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>glmm</span></a> <a href="https://mastodon.social/tags/gaussian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>gaussian</span></a> <a href="https://mastodon.social/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://mastodon.social/tags/r" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>r</span></a> <a href="https://mastodon.social/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a></p><p><a href="https://drizopoulos.github.io/GLMMadaptive/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">drizopoulos.github.io/GLMMadap</span><span class="invisible">tive/</span></a></p>
Karsten Schmidt<p>Added a convolution kernel filtering operator for polygons/polylines to <a href="https://thi.ng/geom" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">thi.ng/geom</span><span class="invisible"></span></a>, incl. some useful preset kernels: box filter, triangle, gaussian, all with configurable size...</p><p>The image shows effects of various kernel sizes &amp; iterations. Unlike with subdivision smoothing, here each version has the exact same number of vertices, only their positions are impacted: orange = box, magenta = triangle, blue = gaussian</p><p>(For now the operator is only implemented for polygons (also w/ holes) &amp; polylines, but can be extended to other shape types...)</p><p><a href="https://mastodon.thi.ng/tags/ThingUmbrella" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ThingUmbrella</span></a> <a href="https://mastodon.thi.ng/tags/Geometry" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Geometry</span></a> <a href="https://mastodon.thi.ng/tags/Polygon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Polygon</span></a> <a href="https://mastodon.thi.ng/tags/Curve" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Curve</span></a> <a href="https://mastodon.thi.ng/tags/Convolution" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Convolution</span></a> <a href="https://mastodon.thi.ng/tags/Gaussian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Gaussian</span></a> <a href="https://mastodon.thi.ng/tags/TypeScript" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TypeScript</span></a></p>
:mastodon: Mike Amundsen<p>A Glimpse of the Laureate's Work</p><p><a href="https://acrobat.adobe.com/id/urn:aaid:sc:US:71783c3d-4f6f-4556-8f75-5a8d4d5f613b" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">acrobat.adobe.com/id/urn:aaid:</span><span class="invisible">sc:US:71783c3d-4f6f-4556-8f75-5a8d4d5f613b</span></a></p><p>"Michel Talagrand is an expert at understanding and taming complicated random processes. Randomness can arise in a wide range of ways, and Talagrand has explored many different types." -- <a href="https://mastodon.social/tags/MattParker" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MattParker</span></a></p><p><a href="https://mastodon.social/tags/theMind" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>theMind</span></a> <a href="https://mastodon.social/tags/gaussian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>gaussian</span></a> <a href="https://mastodon.social/tags/randomness" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>randomness</span></a></p>
Dr. T.J. Jankun-Kelly<p>So how are these new <a href="https://vis.social/tags/gaussian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>gaussian</span></a> <a href="https://vis.social/tags/splats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>splats</span></a> in <a href="https://vis.social/tags/gfx" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>gfx</span></a> different from the splats of two decades ago?</p>
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