david jon furbish<p>I cannot think of an applied mathematics that is more beautiful and far-reaching, or philosophically wilder, than probability. No, nonlinear dynamics and chaos people, it’s not even close 🤣</p><p><a href="https://mastodon.online/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a><br><a href="https://mastodon.online/tags/mathematics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>mathematics</span></a><br><a href="https://mastodon.online/tags/appliedmathematics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>appliedmathematics</span></a><br><a href="https://mastodon.online/tags/philosophy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>philosophy</span></a><br><a href="https://mastodon.online/tags/philosophyofscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>philosophyofscience</span></a> <br><span class="h-card" translate="no"><a href="https://newsmast.community/@philosophy" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>philosophy@newsmast.community</span></a></span> <br><span class="h-card" translate="no"><a href="https://a.gup.pe/u/philosophy" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>philosophy@a.gup.pe</span></a></span></p>
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#probability
2 posts · 2 participants · 1 post today
Ava<p>Suppose I have a random event with k possible outcomes of equal probability. What distribution (if any) describes the probability of obtaining a specific sequence of length m after n events?</p><p><a href="https://mathstodon.xyz/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://mathstodon.xyz/tags/probabilitydistribution" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probabilitydistribution</span></a> <a href="https://mathstodon.xyz/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a></p>
kazé<p>Dear LazyWeb: is there a C/C++, <a href="https://mastodon.social/tags/RustLang" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>RustLang</span></a> or <a href="https://mastodon.social/tags/Zig" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Zig</span></a> equivalent of <a href="https://mastodon.social/tags/SciPy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SciPy</span></a>’s `stats` module for statistical analysis? Namely:<br> • a collection of common PDFs (probability density functions);<br> • MLE (maximum likelihood estimation) for these common distributions;<br> • KDE (kernel density estimation).</p><p>SciPy’s API is a pleasure to work with. Anything that comes close but usable from C/C++/Rust/Zig would make my life so much easier. Boosts appreciated for visibility.</p><p><a href="https://mastodon.social/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://mastodon.social/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://mastodon.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataScience</span></a></p>
🐜🦅<p>Got the physical copies of my Cambridge element in the mail. A reminder that the whole book is free to download until the end of February: <a href="https://doi.org/10.1017/9781009210171" rel="nofollow noopener noreferrer" target="_blank"><span class="invisible">https://</span><span class="">doi.org/10.1017/9781009210171</span><span class="invisible"></span></a></p><p><a href="https://fediphilosophy.org/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://fediphilosophy.org/tags/philosophyofscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>philosophyofscience</span></a> <a href="https://fediphilosophy.org/tags/confirmation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>confirmation</span></a> <a href="https://fediphilosophy.org/tags/induction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>induction</span></a> <a href="https://fediphilosophy.org/tags/bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayesian</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>Accuracy! To counter regression dilution, a method is to add a constraint on the statistical modeling.<br>Regression Redress restrains bias by segregating the residual values.<br>My article: <a href="http://data.yt/kit/regression-redress.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">http://</span><span class="ellipsis">data.yt/kit/regression-redress</span><span class="invisible">.html</span></a></p><p><a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bias</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/modelEvaluation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modelEvaluation</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/dataLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataLearning</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/distributions" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>distributions</span></a> <a href="https://hachyderm.io/tags/accuracy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>accuracy</span></a> <a href="https://hachyderm.io/tags/RegressionRedress" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>RegressionRedress</span></a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> <a href="https://hachyderm.io/tags/RStats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>RStats</span></a></p>
Ross Kang<p>A post of <span class="h-card" translate="no"><a href="https://mathstodon.xyz/@11011110" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>11011110</span></a></span> has reminded me that (after a year and a half lurking here) it's never too late for me to toot and pin an intro here.</p><p>I am a Canadian mathematician in the Netherlands, and I have been based at the University of Amsterdam since 2022. I also have some rich and longstanding ties to the UK, France, and Japan.</p><p>My interests are somewhere in the nexus of Combinatorics, Probability, and Algorithms. Specifically, I like graph colouring, random graphs, and probabilistic/extremal combinatorics. I have an appreciation for randomised algorithms, graph structure theory, and discrete geometry.</p><p>Around 2020, I began taking a more active role in the community, especially in efforts towards improved fairness and openness in science. I am proud to be part of a team that founded the journal, Innovations in Graph Theory (<a href="https://igt.centre-mersenne.org/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">igt.centre-mersenne.org/</span><span class="invisible"></span></a>), that launched in 2023. (That is probably the main reason I joined mathstodon!) I have also been a coordinator since 2020 of the informal research network, A Sparse (Graphs) Coalition (<a href="https://sparse-graphs.mimuw.edu.pl/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">sparse-graphs.mimuw.edu.pl/</span><span class="invisible"></span></a>), devoted to online collaborative workshops. In 2024, I helped spearhead the MathOA Diamond Open Access Stimulus Fund (<a href="https://www.mathoa.org/diamond-open-access-stimulus-fund/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">mathoa.org/diamond-open-access</span><span class="invisible">-stimulus-fund/</span></a>).</p><p>Until now, my posts have mostly been about scientific publishing and combinatorics.</p><p><a href="https://mathstodon.xyz/tags/introduction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>introduction</span></a> <br><a href="https://mathstodon.xyz/tags/openscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>openscience</span></a> <br><a href="https://mathstodon.xyz/tags/diamondopenaccess" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>diamondopenaccess</span></a> <br><a href="https://mathstodon.xyz/tags/scientificpublishing" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scientificpublishing</span></a> <br><a href="https://mathstodon.xyz/tags/openaccess" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>openaccess</span></a> <br><a href="https://mathstodon.xyz/tags/RemoteConferences" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>RemoteConferences</span></a> <br><a href="https://mathstodon.xyz/tags/combinatorics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>combinatorics</span></a> <br><a href="https://mathstodon.xyz/tags/graphtheory" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>graphtheory</span></a> <br><a href="https://mathstodon.xyz/tags/ExtremalCombinatorics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ExtremalCombinatorics</span></a> <br><a href="https://mathstodon.xyz/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a></p>
🐜🦅<p>I posted this yesterday, but I should have noted that my Cambridge Element ‘Probability and Inductive Logic’ is free to download for the next four weeks. Get amongst it!</p><p><a href="https://doi.org/10.1017/9781009210171" rel="nofollow noopener noreferrer" target="_blank"><span class="invisible">https://</span><span class="">doi.org/10.1017/9781009210171</span><span class="invisible"></span></a></p><p><a href="https://fediphilosophy.org/tags/philosophy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>philosophy</span></a> <a href="https://fediphilosophy.org/tags/Bayesianism" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesianism</span></a> <a href="https://fediphilosophy.org/tags/induction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>induction</span></a> <a href="https://fediphilosophy.org/tags/confirmation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>confirmation</span></a> <a href="https://fediphilosophy.org/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://fediphilosophy.org/tags/philosophyofscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>philosophyofscience</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>How to assess a statistical model?<br>How to choose between variables?</p><p>Pearson's <a href="https://hachyderm.io/tags/correlation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>correlation</span></a> is irrelevant if you suspect that the relationship is not a straight line.</p><p>If monotonic relationship:<br>"<a href="https://hachyderm.io/tags/Spearman" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Spearman</span></a>’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".<br>"<a href="https://hachyderm.io/tags/Kendall" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Kendall</span></a>’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."<br>Ref: <a href="https://statisticseasily.com/kendall-tau-b-vs-spearman/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticseasily.com/kendall-t</span><span class="invisible">au-b-vs-spearman/</span></a></p><p><a href="https://hachyderm.io/tags/normality" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>normality</span></a> <a href="https://hachyderm.io/tags/normalDistribution" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>normalDistribution</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/modelEvaluation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modelEvaluation</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/dataLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataLearning</span></a> <a href="https://hachyderm.io/tags/featureEngineering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>featureEngineering</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/Pearson" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Pearson</span></a> <a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bias</span></a> <a href="https://hachyderm.io/tags/regressionRedress" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>regressionRedress</span></a> <a href="https://hachyderm.io/tags/distributions" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>distributions</span></a></p>
Markus Redeker<p>New blog post: A short solution to the Monty Hall problem that I have not seen elsewhere (<a href="https://functor.network/user/414/entry/867" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">functor.network/user/414/entry</span><span class="invisible">/867</span></a>).</p><p><a href="https://mathstodon.xyz/tags/WordsAndSomeFormulas" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>WordsAndSomeFormulas</span></a> <a href="https://mathstodon.xyz/tags/MontyHall" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MontyHall</span></a> <a href="https://mathstodon.xyz/tags/Probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Probability</span></a> <a href="https://mathstodon.xyz/tags/Mathematics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Mathematics</span></a></p>
Cheng Soon Ong<p>"... probability probably does not exist — but it is often useful to act as if it does."<br>David Spiegelhalter provides a short essay that touches on the main aspects of the elusive idea of probability.<br><a href="https://www.nature.com/articles/d41586-024-04096-5" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">nature.com/articles/d41586-024</span><span class="invisible">-04096-5</span></a></p><p>His book on Uncertainty just came out yesterday, which I expect will explain these ideas in more detail.<br><a href="https://www.penguin.com.au/books/the-art-of-uncertainty-9780241658628" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">penguin.com.au/books/the-art-o</span><span class="invisible">f-uncertainty-9780241658628</span></a></p><p><a href="https://masto.ai/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MachineLearning</span></a> <a href="https://masto.ai/tags/Statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Statistics</span></a> <a href="https://masto.ai/tags/Probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Probability</span></a> <a href="https://masto.ai/tags/scicomm" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scicomm</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>Redressing <a href="https://hachyderm.io/tags/Bias" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bias</span></a>: "Correlation Constraints for Regression Models":<br>Treder et al (2021) <a href="https://doi.org/10.3389/fpsyt.2021.615754" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.3389/fpsyt.2021.615</span><span class="invisible">754</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/skLearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>skLearn</span></a> <a href="https://hachyderm.io/tags/scikitLearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scikitLearn</span></a> <a href="https://hachyderm.io/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AIDev</span></a></p>
Eric Maugendre<p>"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight."<br>Longford (2005) <a href="http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">http://www.</span><span class="ellipsis">stat.columbia.edu/~gelman/stuf</span><span class="invisible">f_for_blog/longford.pdf</span></a></p><p><a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/nullHypothesis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>nullHypothesis</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/pValues" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pValues</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/statisticalLiteracy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statisticalLiteracy</span></a> <a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bias</span></a> <a href="https://hachyderm.io/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>inference</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>linearRegression</span></a></p>
Dr. Anna Latour<p>I'm teaching my first lecture at the new job today, about probabilistic logic programming, probabilistic inference, and (weighted) model counting.</p><p>Some of the required reading is a paper (<a href="https://eccc.weizmann.ac.il/eccc-reports/2003/TR03-003/index.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">eccc.weizmann.ac.il/eccc-repor</span><span class="invisible">ts/2003/TR03-003/index.html</span></a>) that was written by a great mentor of mine, prof. dr. Fahiem Bacchus. He passed away just over 2 years ago, and I am honoured to keep his memory alive by teaching his ideas to a new generation of students. Hope to do him proud. 🌱 </p><p>Please send good vibes? 🥺 </p><p><a href="https://mathstodon.xyz/tags/AcademicChatter" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AcademicChatter</span></a> <a href="https://mathstodon.xyz/tags/AcademicLife" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AcademicLife</span></a> <a href="https://mathstodon.xyz/tags/AcademicMastodon" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AcademicMastodon</span></a> <a href="https://mathstodon.xyz/tags/Teaching" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Teaching</span></a> <a href="https://mathstodon.xyz/tags/Probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Probability</span></a> <a href="https://mathstodon.xyz/tags/ProbabilisticInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ProbabilisticInference</span></a> <a href="https://mathstodon.xyz/tags/Probabilities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Probabilities</span></a> <a href="https://mathstodon.xyz/tags/Logic" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Logic</span></a> <a href="https://mathstodon.xyz/tags/LogicProgramming" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>LogicProgramming</span></a> <a href="https://mathstodon.xyz/tags/PropositionalModelCounting" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PropositionalModelCounting</span></a> <a href="https://mathstodon.xyz/tags/ProbabilisticLogicProgramming" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ProbabilisticLogicProgramming</span></a> <a href="https://mathstodon.xyz/tags/ModelCounting" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ModelCounting</span></a> <a href="https://mathstodon.xyz/tags/PropositionalLogic" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PropositionalLogic</span></a> <a href="https://mathstodon.xyz/tags/WeightedModelCounting" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>WeightedModelCounting</span></a> <a href="https://mathstodon.xyz/tags/DPLL" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DPLL</span></a> <a href="https://mathstodon.xyz/tags/BayesianProbability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BayesianProbability</span></a> <a href="https://mathstodon.xyz/tags/BayesNets" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BayesNets</span></a> <a href="https://mathstodon.xyz/tags/BasianStatistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BasianStatistics</span></a> <a href="https://mathstodon.xyz/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BayesianInference</span></a> <a href="https://mathstodon.xyz/tags/BayesianNetworks" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BayesianNetworks</span></a> <a href="https://mathstodon.xyz/tags/KnowledgeCompilation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>KnowledgeCompilation</span></a> <a href="https://mathstodon.xyz/tags/DecisionDiagrams" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DecisionDiagrams</span></a> <a href="https://mathstodon.xyz/tags/BinaryDecisionDiagrams" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BinaryDecisionDiagrams</span></a></p>
Eric Maugendre<p>Feature Selection in Python; a script ready to use: <a href="https://johfischer.com/2021/08/06/correlation-based-feature-selection-in-python-from-scratch/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">johfischer.com/2021/08/06/corr</span><span class="invisible">elation-based-feature-selection-in-python-from-scratch/</span></a></p><p><a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>interpretability</span></a> <a href="https://hachyderm.io/tags/featureSelection" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>featureSelection</span></a> <a href="https://hachyderm.io/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/bigData" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bigData</span></a> <a href="https://hachyderm.io/tags/classification" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>classification</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/Schusterbauer" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Schusterbauer</span></a> <a href="https://hachyderm.io/tags/inference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>inference</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AIDev</span></a></p>
Daniel Lakeland<p>We fetishize the real numbers. I mean, they're pretty good, nothing terrible about them. But hyperreals are just way cooler. And hyperreals give you a much nicer way to think about probability on continuous regions. Just divide the space up into an infinitesimal grid and call it good. These are the points we can have as outcomes, our measurements will always be finite precision anyway. Continuum is an approximation of reality not the other way around. <a href="https://mastodon.sdf.org/tags/math" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>math</span></a> <a href="https://mastodon.sdf.org/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://mastodon.sdf.org/tags/hyperreals" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>hyperreals</span></a></p>
Eric Maugendre<p>Surveys, coincidences, statistical significance 🧵</p><p>"What Educated Citizens Should Know About Statistics and Probability"<br>By Jessica Utts, in 2003: <a href="https://ics.uci.edu/~jutts/AmerStat2003.pdf" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">ics.uci.edu/~jutts/AmerStat200</span><span class="invisible">3.pdf</span></a> via <span class="h-card" translate="no"><a href="https://hachyderm.io/@hrefna" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>hrefna</span></a></span> </p><p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/edutooters" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>edutooters</span></a></span></p><p><a href="https://hachyderm.io/tags/nullHypothesis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>nullHypothesis</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/pValues" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pValues</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/education" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>education</span></a> <a href="https://hachyderm.io/tags/higherEd" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>higherEd</span></a> <a href="https://hachyderm.io/tags/statisticalLiteracy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statisticalLiteracy</span></a> <a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bias</span></a> <a href="https://hachyderm.io/tags/media" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>media</span></a> <a href="https://hachyderm.io/tags/causalInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causalInference</span></a></p>
Bibliolater 📚 📜 🖋<p>🔴 🎥 A bizarre probability fact </p><p><a href="https://qoto.org/tags/Video" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Video</span></a> length: two minutes and forty eight seconds.</p><p><a href="https://youtu.be/Pny70rNPJLk" rel="nofollow noopener noreferrer" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/Pny70rNPJLk</span><span class="invisible"></span></a></p><p><a href="https://qoto.org/tags/Probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Probability</span></a> <a href="https://qoto.org/tags/Maths" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Maths</span></a> <a href="https://qoto.org/tags/Mathematics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Mathematics</span></a> <a href="https://qoto.org/tags/Math" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Math</span></a> <a href="https://qoto.org/tags/Fact" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Fact</span></a></p>
Bibliolater 📚 📜 🖋<p>🔴 A numerical evaluation of the Finite Monkeys Theorem</p><p>"From this, we can see that all but the most trivial of phrases will, in fact, almost certainly never be produced during the lifespan of our universe. There are many orders of magnitude difference between the expected numbers of keys to be randomly pressed before Shakespeare's works are reproduced and the number of keystrokes until the universe collapses into thermodynamic equilibrium..."</p><p>Woodcock, S. and Falletta, J. (2024) 'A numerical evaluation of the Finite Monkeys Theorem,' Franklin Open, p. 100171. <a href="https://doi.org/10.1016/j.fraope.2024.100171" rel="nofollow noopener noreferrer" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.1016/j.fraope.2024.</span><span class="invisible">100171</span></a>.</p><p><a href="https://qoto.org/tags/OpenAccess" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>OpenAccess</span></a> <a href="https://qoto.org/tags/OA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>OA</span></a> <a href="https://qoto.org/tags/Research" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Research</span></a> <a href="https://qoto.org/tags/Article" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Article</span></a> <a href="https://qoto.org/tags/DOI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DOI</span></a> <a href="https://qoto.org/tags/Maths" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Maths</span></a> <a href="https://qoto.org/tags/Mathematics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Mathematics</span></a> <a href="https://qoto.org/tags/Math" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Math</span></a> <a href="https://qoto.org/tags/Infinity" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Infinity</span></a> <a href="https://qoto.org/tags/Combinatorics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Combinatorics</span></a> <a href="https://qoto.org/tags/Probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Probability</span></a> <a href="https://qoto.org/tags/Monkeys" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Monkeys</span></a> <a href="https://qoto.org/tags/Shakespeare" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Shakespeare</span></a> <a href="https://qoto.org/tags/Academia" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Academia</span></a> <a href="https://qoto.org/tags/Academic" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Academic</span></a> <a href="https://qoto.org/tags/Academics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Academics</span></a></p>
Natasha Jay 🇪🇺<p>I love that someone actually remodelled the Infinite Monkeys Typing Shakespeare theorem with practical assumptions. They must be hiding somewhere in the <a href="https://tech.lgbt/tags/Fediverse" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Fediverse</span></a>? </p><p>Come out and admit it! 😂</p><p>“We decided to look at the probability of a given string of letters being typed by a finite number of monkeys within a finite time period consistent with estimates for the lifespan of our universe”</p><p>"But the chances of a monkey succeeding in typing even a short phrase quickly become vanishingly small, with the phrase “I chimp, therefore I am” coming in at one in 10 million billion billion"</p><p>Assumptions here:<br><a href="https://www.independent.co.uk/news/science/monkey-type-shakespeare-theory-infinity-universe-b2639492.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">independent.co.uk/news/science</span><span class="invisible">/monkey-type-shakespeare-theory-infinity-universe-b2639492.html</span></a></p><p><a href="https://tech.lgbt/tags/mathematics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>mathematics</span></a> <a href="https://tech.lgbt/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a></p>
Eric Maugendre<p>"In <a href="https://mas.to/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> theory, a log-normal (or <a href="https://mas.to/tags/lognormal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>lognormal</span></a>) distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. Thus, if the random variable X is log-normally distributed, then Y = ln(X) has a normal distribution."</p><p>"It is a convenient and useful model for measurements in exact and engineering sciences, as well as medicine, economics […], energies, concentrations, lengths, prices".</p><p><a href="https://en.wikipedia.org/wiki/Log-normal_distribution" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">en.wikipedia.org/wiki/Log-norm</span><span class="invisible">al_distribution</span></a></p><p><a href="https://mas.to/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://mas.to/tags/finance" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>finance</span></a> <a href="https://mas.to/tags/modeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modeling</span></a></p>
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