MediaFaro News Digest<p>Sunken British superyacht Bayesian is raised from the seabed.</p><p>A superyacht that sank off the coast of the Italian island of Sicily last year has been raised from the seabed by a specialist salvage team.</p><p>Seven of the 22 people on board died in the sinking, including the vessel's owner, British tech tycoon Mike Lynch and his 18-year-old daughter.</p><p>The cause of the sinking is still under investigation.</p><p><a href="https://mediafaro.org/article/20250620-sunken-british-superyacht-bayesian-is-raised-from-the-seabed?mf_channel=mastodon&action=forward" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">mediafaro.org/article/20250620</span><span class="invisible">-sunken-british-superyacht-bayesian-is-raised-from-the-seabed?mf_channel=mastodon&action=forward</span></a></p><p><a href="https://mastodon.mediafaro.org/tags/Italy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Italy</span></a> <a href="https://mastodon.mediafaro.org/tags/UK" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>UK</span></a> <a href="https://mastodon.mediafaro.org/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> <a href="https://mastodon.mediafaro.org/tags/MikeLynch" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MikeLynch</span></a></p>
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#bayesian
2 posts · 2 participants · 1 post today
pglpm<p>Interested in trying out *Bayesian nonparametrics* for your statistical research?</p><p>I'd be very grateful if people tried out this R package for Bayesian nonparametric population inference, called "inferno" :</p><p><<a href="https://pglpm.github.io/inferno/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">pglpm.github.io/inferno/</span><span class="invisible"></span></a>></p><p>It is especially addressed to clinical and medical researchers, and allows for thorough statistical studies of subpopulations or subgroups.</p><p>Installation instructions are here: <<a href="https://pglpm.github.io/inferno/index.html#installation" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">pglpm.github.io/inferno/index.</span><span class="invisible">html#installation</span></a>>.</p><p>A step-by-step tutorial, guiding you through an example analysis of a simple dataset, is here: <<a href="https://pglpm.github.io/inferno/articles/vignette_start.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">pglpm.github.io/inferno/articl</span><span class="invisible">es/vignette_start.html</span></a>>.</p><p>The package has already been tested and used in concrete research about Alzheimer's Disease, Parkinson's Disease, drug discovery, and applications to machine learning.</p><p>Feedback is very welcome. If you find the package useful, feel free to advertise it a little :)</p><p><a href="https://c.im/tags/rstats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>rstats</span></a> <a href="https://c.im/tags/bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayesian</span></a> <a href="https://c.im/tags/bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayes</span></a> <a href="https://c.im/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://c.im/tags/medicine" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>medicine</span></a> <a href="https://c.im/tags/datascience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>datascience</span></a></p>
PLOS Biology<p>Rewarding animals to accurately report their subjective <a href="https://fediscience.org/tags/percept" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>percept</span></a> is challenging. This study formalizes this problem and overcomes it with a <a href="https://fediscience.org/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> method for estimating an animal’s subjective percept in real time during the experiment <span class="h-card" translate="no"><a href="https://fediscience.org/@PLOSBiology" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>PLOSBiology</span></a></span> <a href="https://plos.io/3HaxiuB" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">plos.io/3HaxiuB</span><span class="invisible"></span></a></p>
Daniel Lakeland<p><a href="https://aeon.co/essays/no-schrodingers-cat-is-not-alive-and-dead-at-the-same-time" rel="nofollow noopener noreferrer" target="_blank"><span class="invisible">https://</span><span class="ellipsis">aeon.co/essays/no-schrodingers</span><span class="invisible">-cat-is-not-alive-and-dead-at-the-same-time</span></a></p><p>This is a pretty good article for showing how confused the interpretation of QM is. And its a good article to understand why i personally side with Bohm and Bell in thinking the pilot wave theory is the one most reasonable to believe. Because the pilot wave theory has the following quality. The theory is a mapping from initial position at time t=0 to final position at time t=1...Its deterministic, but our knowledge of the initial condition is not<br><a href="https://mastodon.sdf.org/tags/quantum" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>quantum</span></a> <a href="https://mastodon.sdf.org/tags/bohm" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bohm</span></a> <a href="https://mastodon.sdf.org/tags/bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayesian</span></a></p>
tagesschau<p>Vorläufiger Bericht: Luxusjacht "Bayesian" sank wegen extremen Winds</p><p>Bei dem Untergang der "Bayesian" vor Sizilien kamen im vergangenen Jahr sieben Menschen ums Leben. Nun gibt ein vorläufiger Bericht Hinweise auf die Unglücksursache der Luxusjacht, die eigentlich als "unsinkbar" galt.</p><p>➡️ <a href="https://www.tagesschau.de/ausland/europa/bayesian-bericht-unglueck-100.html?at_medium=mastodon&at_campaign=tagesschau.de" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">tagesschau.de/ausland/europa/b</span><span class="invisible">ayesian-bericht-unglueck-100.html?at_medium=mastodon&at_campaign=tagesschau.de</span></a></p><p><a href="https://ard.social/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> <a href="https://ard.social/tags/Schiffsungl%C3%BCck" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Schiffsunglück</span></a></p>
safest_integer<p>The new <a href="https://mastodon.social/tags/pope" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pope</span></a> has a degree in <a href="https://mastodon.social/tags/mathematics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>mathematics</span></a> and wrote a <a href="https://mastodon.social/tags/PhD" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PhD</span></a> thesis on "The role of the local prior" which I assume is a contribution to <a href="https://mastodon.social/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> <a href="https://mastodon.social/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a>. </p><p><a href="https://en.m.wikipedia.org/wiki/Pope_Leo_XIV" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">en.m.wikipedia.org/wiki/Pope_L</span><span class="invisible">eo_XIV</span></a></p>
💧🌏 Greg Cocks<p>Observations Reveal Changing Coastal Storm Extremes Around The United States<br>--<br><a href="https://doi.org/10.1038/s41558-025-02315-z" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.1038/s41558-025-023</span><span class="invisible">15-z</span></a> <-- shared paper<br>--<br><a href="https://techhub.social/tags/GIS" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>GIS</span></a> <a href="https://techhub.social/tags/spatial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>spatial</span></a> <a href="https://techhub.social/tags/mapping" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>mapping</span></a> <a href="https://techhub.social/tags/extremeweather" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>extremeweather</span></a> <a href="https://techhub.social/tags/coast" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>coast</span></a> <a href="https://techhub.social/tags/coastal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>coastal</span></a> <a href="https://techhub.social/tags/spatialanalysis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>spatialanalysis</span></a> <a href="https://techhub.social/tags/spatiotemporal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>spatiotemporal</span></a> <a href="https://techhub.social/tags/model" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>model</span></a> <a href="https://techhub.social/tags/modeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modeling</span></a> <a href="https://techhub.social/tags/communities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>communities</span></a> <a href="https://techhub.social/tags/publicsafety" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>publicsafety</span></a> <a href="https://techhub.social/tags/climatechange" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>climatechange</span></a> <a href="https://techhub.social/tags/stormsurge" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stormsurge</span></a> <a href="https://techhub.social/tags/USA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>USA</span></a> <a href="https://techhub.social/tags/flood" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>flood</span></a> <a href="https://techhub.social/tags/flooding" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>flooding</span></a> <a href="https://techhub.social/tags/risk" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>risk</span></a> <a href="https://techhub.social/tags/hazard" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>hazard</span></a> <a href="https://techhub.social/tags/damage" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>damage</span></a> <a href="https://techhub.social/tags/infrastructure" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>infrastructure</span></a> <a href="https://techhub.social/tags/cost" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>cost</span></a> <a href="https://techhub.social/tags/economics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>economics</span></a> <a href="https://techhub.social/tags/mitigation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>mitigation</span></a> <a href="https://techhub.social/tags/insurance" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>insurance</span></a> <a href="https://techhub.social/tags/sealevel" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>sealevel</span></a> <a href="https://techhub.social/tags/SLR" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SLR</span></a> <a href="https://techhub.social/tags/sealevelrise" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>sealevelrise</span></a> <a href="https://techhub.social/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> <a href="https://techhub.social/tags/hierarchical" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>hierarchical</span></a> <a href="https://techhub.social/tags/framework" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>framework</span></a> <a href="https://techhub.social/tags/tideguage" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>tideguage</span></a> <a href="https://techhub.social/tags/tide" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>tide</span></a> <a href="https://techhub.social/tags/tidal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>tidal</span></a> <a href="https://techhub.social/tags/water" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>water</span></a> <a href="https://techhub.social/tags/hydrology" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>hydrology</span></a> <a href="https://techhub.social/tags/hydrography" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>hydrography</span></a> <a href="https://techhub.social/tags/extremes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>extremes</span></a> <a href="https://techhub.social/tags/intensity" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>intensity</span></a> <a href="https://techhub.social/tags/monitoring" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>monitoring</span></a></p>
Alex Holcombe<p>A <a href="https://fediscience.org/tags/bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayesian</span></a> blogpost, by two of my undergraduate students! It's their report on their learning Bayesian modeling by applying it to my lab's data.<br><a href="https://alexholcombe.github.io/brms_psychometric_variableGuessRate_lapseRate/DenisonBlog.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">alexholcombe.github.io/brms_ps</span><span class="invisible">ychometric_variableGuessRate_lapseRate/DenisonBlog.html</span></a><br>Summary: we learned to use brms. But had trouble when we added more than one or two factors to the model. Little idea why; haven't had time to tinker much with that.</p>
Dr Mircea Zloteanu ☃️<p>If I collect some data for a new treatment, but don't collect a control group, can I use the data from an older study that has the control condition? If so, how would I integrate this into my analysis? and what is the name for this? historical control? </p><p><a href="https://mastodon.social/tags/statstodon" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statstodon</span></a> <a href="https://mastodon.social/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> <a href="https://mastodon.social/tags/research" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>research</span></a> <a href="https://mastodon.social/tags/bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayesian</span></a></p>
Angela Glansbury 🚽<p>what's your funniest accidental death of 2024 <a href="https://todon.nl/tags/2024Poll" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>2024Poll</span></a> <a href="https://todon.nl/tags/poll" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>poll</span></a> </p><p>Shipping tycoon <a href="https://todon.nl/tags/AngelaChao" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AngelaChao</span></a> drink driving her <a href="https://todon.nl/tags/Tesla" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Tesla</span></a> into a pond and drowning? 🌊 🚗 </p><p>Tech entrepreneur <a href="https://todon.nl/tags/MikeLynch" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MikeLynch</span></a> being accidentally assassinated by <a href="https://todon.nl/tags/HP" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>HP</span></a> when his 56m yacht the <a href="https://todon.nl/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> sank off the coast of Sicily?🌊⛵ </p><p><a href="https://todon.nl/tags/BrianThompson" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BrianThompson</span></a> the late healthcare CEO who accidentally intercepted a mysterious bullet fired probably from a <a href="https://todon.nl/tags/Welrod" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Welrod</span></a> or <a href="https://todon.nl/tags/StationSIX9" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>StationSIX9</span></a> ⚰️🤔 </p><p>Billionaire founder of high street fashion chain <a href="https://todon.nl/tags/Mango" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Mango</span></a> <a href="https://todon.nl/tags/IsakAndic" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>IsakAndic</span></a> who fell into a ravine🏔️🥴</p>
Daniel Lakeland<p>I got an email from the author promoting this benchmark comparison of <a href="https://mastodon.sdf.org/tags/Julialang" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Julialang</span></a> + StanBlocks + <a href="https://mastodon.sdf.org/tags/Enzyme" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Enzyme</span></a> vs <a href="https://mastodon.sdf.org/tags/Stan" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Stan</span></a> runtimes.</p><p>StanBlocks is a macro package for Julia that mimics the structure of a Stan program. This is the first I've heard about it.</p><p>A considerable number of these models are faster in Julia than Stan, maybe even most of them. </p><p><a href="https://nsiccha.github.io/StanBlocks.jl/performance.html" rel="nofollow noopener noreferrer" target="_blank"><span class="invisible">https://</span><span class="ellipsis">nsiccha.github.io/StanBlocks.j</span><span class="invisible">l/performance.html</span></a></p><p><a href="https://mastodon.sdf.org/tags/bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayes</span></a> <a href="https://mastodon.sdf.org/tags/bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayesian</span></a> <a href="https://mastodon.sdf.org/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a></p>
Daniel Lakeland<p>Everyone thinks big data should be better. If you have a good model that makes good predictions then often more data is an enormous nuisance. The posterior distributions are so narrow that you can never sample properly. It's like finding a hydrogen atom in your bedroom. The thing is just too damn small. So anyway we are trying tempering for our migration model just so we can get convergence. I don't care about tiny uncertainty intervals. just be near the right answer. <a href="https://mastodon.sdf.org/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> <a href="https://mastodon.sdf.org/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a></p>
Christos Argyropoulos MD PhD<p>A couple of years ago, we took various tools for the analysis of repeated measures in very large big data for a ride. </p><p>We tested various pckages in <a href="https://mastodon.social/tags/rstats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>rstats</span></a> and <a href="https://mastodon.social/tags/stan" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stan</span></a> and it turns out that the best tool for the task was Nelder's hierarchical likelihood (which has a <a href="https://mastodon.social/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> interpretation too)</p><p>The repository is <a href="https://bitbucket.org/chrisarg/laplaceapproximationandhyperkalemia/src/master/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">bitbucket.org/chrisarg/laplace</span><span class="invisible">approximationandhyperkalemia/src/master/</span></a> <br>Link to the paper 👇<br><a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC8310602/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">pmc.ncbi.nlm.nih.gov/articles/</span><span class="invisible">PMC8310602/</span></a></p><p>During the process we bracketed what (we think should be the reference range for the serum potassium)</p>
pglpm<p><span class="h-card" translate="no"><a href="https://lgbtqia.space/@AeonCypher" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>AeonCypher</span></a></span> <span class="h-card" translate="no"><a href="https://mastodon.world/@paninid" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>paninid</span></a></span> </p><p>"A p-value is an <a href="https://c.im/tags/estimate" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>estimate</span></a> of p(Data | Null Hypothesis). " – not correct. A p-value is an estimate of</p><p>p(Data or other imagined data | Null Hypothesis)</p><p>so not even just of the actual data you have. Which is why p-values depend on your stopping rule (and do not satisfy the "likelihood principle"). In this regard, see Jeffreys's quote below.</p><p>Imagine you design an experiment this way: "I'll test 10 subjects, and in the meantime I apply for a grant. At the time the 10th subject is tested, I'll know my application's outcome. If the outcome is positive, I'll test 10 more subjects; if it isn't, I'll stop". Not an unrealistic situation.</p><p>With this stopping rule, your p-value will depend on the probability that you get the grant. This is not a joke.</p><p>"*What the use of P implies, therefore, is that a hypothesis that may be true may be rejected because it has not predicted observable results that have not occurred.* This seems a remarkable procedure. On the face of it the fact that such results have not occurred might more reasonably be taken as evidence for the law, not against it." – H. Jeffreys, "Theory of Probability" § VII.7.2 (emphasis in the original) <<a href="https://doi.org/10.1093/oso/9780198503682.001.0001" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.1093/oso/9780198503</span><span class="invisible">682.001.0001</span></a>>.</p><p><a href="https://c.im/tags/bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayesian</span></a> <a href="https://c.im/tags/bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayes</span></a> <a href="https://c.im/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a></p>
➴➴➴Æ🜔Ɲ.Ƈꭚ⍴𝔥єɼ👩🏻💻<p><span class="h-card" translate="no"><a href="https://mastodon.world/@paninid" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>paninid</span></a></span> p-values, to a large extent, exist because calculating the posterior is computationally expensive. Not all fields use the .05 cutoff.</p><p>A p-value is an <a href="https://lgbtqia.space/tags/estimate" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>estimate</span></a> of p(Data | Null Hypothesis). If the two <a href="https://lgbtqia.space/tags/hypotheses" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>hypotheses</span></a> are equally likely and they are mutually exclusive and they are closed over the <a href="https://lgbtqia.space/tags/hypothesis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>hypothesis</span></a> space, then this is the same as p(Hypothesis | Data).</p><p>Meaning, under certain assumption, the p-value does represent the actually probability of being wrong. </p><p>However, given modern computers, there is no reason that <a href="https://lgbtqia.space/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> odds-ratios can't completely replace their usage and avoid the many many problems with p-values.</p>
Tim Redick :ros: :julia:<p>Small advertisement for my Ph.D. thesis and code, focused on <a href="https://fosstodon.org/tags/computervision" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>computervision</span></a> for <a href="https://fosstodon.org/tags/robotics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>robotics</span></a>.<br>Using <a href="https://fosstodon.org/tags/julialang" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>julialang</span></a> to implement <a href="https://fosstodon.org/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> inference algorithms for the 6D pose estimation of known objects in depth images. <br>TLDR: it works even with occlusions; needs <1sec on a GPU; does not need training; future research could focus on including color images / semantic information since SOA performs much better if color images are available.<br>doc: <a href="https://publications.rwth-aachen.de/record/985219" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">publications.rwth-aachen.de/re</span><span class="invisible">cord/985219</span></a><br>code: <a href="https://github.com/rwth-irt/BayesianPoseEstimation.jl" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/rwth-irt/BayesianPo</span><span class="invisible">seEstimation.jl</span></a></p>
-0--1-<p><span class="h-card" translate="no"><a href="https://mastodon.social/@HistoPol" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>HistoPol</span></a></span> <a href="https://mastodon.social/tags/MultiDimensionalScaling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MultiDimensionalScaling</span></a> and <a href="https://mastodon.social/tags/NonMetricClusterAnalysis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>NonMetricClusterAnalysis</span></a> as well as <a href="https://mastodon.social/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> models are where I am headed. But I need to make sure my Data is robust with contributing factors. Parents and their religions, Existing Froundational texts are critical before I begin the analysis. <a href="https://beast.community/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">beast.community/</span><span class="invisible"></span></a></p>
IT News<p>Pong in a Petri Dish: Teasing Out How Brains Work - Experimental setup for the EAP hydrogel free energy principle test. (Credit: Vince... - <a href="https://hackaday.com/2024/09/14/pong-in-a-petri-dish-teasing-out-how-brains-work/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">hackaday.com/2024/09/14/pong-i</span><span class="invisible">n-a-petri-dish-teasing-out-how-brains-work/</span></a> <a href="https://schleuss.online/tags/biologicalneuralnetwork" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>biologicalneuralnetwork</span></a> <a href="https://schleuss.online/tags/electroactivepolymers" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>electroactivepolymers</span></a> <a href="https://schleuss.online/tags/bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayesian</span></a> <a href="https://schleuss.online/tags/science" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>science</span></a></p>
Cat West<p><a href="https://mastodon.social/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> <a href="https://mastodon.social/tags/Karma" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Karma</span></a> <a href="https://mastodon.social/tags/Arrogance" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Arrogance</span></a>: Stir, bring to a boil and letter RIP<br><a href="https://apple.news/Ao1y34QTFRlC55gTkHL8qlQ" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">apple.news/Ao1y34QTFRlC55gTkHL</span><span class="invisible">8qlQ</span></a></p>
Lazarou Monkey Terror 🚀💙🌈<p>You're not Rich, you're not a human being worth giving a fuck about. </p><p><a href="https://mastodon.social/tags/MikeLynch" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MikeLynch</span></a> <a href="https://mastodon.social/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> <a href="https://mastodon.social/tags/Italy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Italy</span></a> <a href="https://mastodon.social/tags/NoBillionaires" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>NoBillionaires</span></a> <a href="https://mastodon.social/tags/SmallBoats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SmallBoats</span></a> <a href="https://mastodon.social/tags/Refugees" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Refugees</span></a></p>
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