Nick Byrd, Ph.D.<p>People were less averse to <a href="https://nerdculture.de/tags/risk" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>risk</span></a> (d = 0.4) when making <a href="https://nerdculture.de/tags/prenatalTesting" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>prenatalTesting</span></a> decisions in their SECOND <a href="https://nerdculture.de/tags/language" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>language</span></a> — even when they seemed to understand the relevant information.</p><p><a href="https://doi.org/10.1002/bdm.70016" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">doi.org/10.1002/bdm.70016</span><span class="invisible"></span></a></p><p><a href="https://nerdculture.de/tags/parenting" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>parenting</span></a> <a href="https://nerdculture.de/tags/cogSci" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>cogSci</span></a> <a href="https://nerdculture.de/tags/medicine" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>medicine</span></a> <a href="https://nerdculture.de/tags/genetics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>genetics</span></a> <a href="https://nerdculture.de/tags/edu" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>edu</span></a> <a href="https://nerdculture.de/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://nerdculture.de/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> <a href="https://nerdculture.de/tags/linguistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>linguistics</span></a> <a href="https://nerdculture.de/tags/econ" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>econ</span></a></p>
Recent searches
No recent searches
Search options
Only available when logged in.
eupolicy.social is one of the many independent Mastodon servers you can use to participate in the fediverse.
This Mastodon server is a friendly and respectful discussion space for people working in areas related to EU policy. When you request to create an account, please tell us something about you.
Administered by:
Server stats:
227active users
eupolicy.social: About · Profiles directory · Privacy policy
Mastodon: About · Get the app · Keyboard shortcuts · View source code · v4.3.6
#probability
0 posts · 0 participants · 0 posts today
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>
Longreads<p>"Our world should be at its most analyzable, explicable — but still it can feel like sorcery."</p><p>Eric Boodman for New York magazine: <a href="https://longreads.com/2025/04/08/does-luck-exist/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">longreads.com/2025/04/08/does-</span><span class="invisible">luck-exist/</span></a> </p><p><a href="https://mastodon.world/tags/Longreads" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Longreads</span></a> <a href="https://mastodon.world/tags/Luck" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Luck</span></a> <a href="https://mastodon.world/tags/Chance" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Chance</span></a> <a href="https://mastodon.world/tags/Probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Probability</span></a> <a href="https://mastodon.world/tags/Philosophy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Philosophy</span></a>#Superstition</p>
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/Rust" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Rust</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>
Ross Gayler<p>Some probability/maths/optimisation questions for the Fedi-hive mind:</p><p>Bayes' Theorem is<br>P(H | E) = P(E | H) P(H) / P(E)<br>where H and E are events (that I have labelled for my mnemonic convenience to suggest Hypothesis and Evidence, but they're just events).</p><p>Assume that:<br>* There is some fixed database of records with a fixed set of fields.<br>* The events H and E are predicates of individual database records.<br>* The event predicates are functions of the field values in the record being evaluated.<br>* We are interpreting the relative frequency of the event predicate being true over all the record in the database as the probability of the event defined by the predicate.</p><p>The typical statement of Bayes' Theorem appears to assume that the definitions of the events H and E are fixed and given, and the only thing of interest is how to calculate with them.</p><p>1. Does it make sense to have a fixed definition of H and search over the space of possible definitions of E to maximise P(H | E)?</p><p>2. Is there a name for this? (I presume it's been suggested many times already.) Is it abductive inference because you're trying to find the "best explanation" of H?</p><p>3. Are there constraints that need to be placed on the optimisation? (a. You wouldn't want the E definition to be a copy of or equivalent to the H definition. b. You wouldn't want the E definition to be some degenerate case, e.g. with P(E) vanishingly small. c. You probably want some regularisation penalty that prefers simple definitions of E over more complex ones.</p><p>Any comments on this and pointers into the literature would be greatly appreciated.</p><p><a href="https://aus.social/tags/math" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>math</span></a> <a href="https://aus.social/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://aus.social/tags/Bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayes</span></a> <a href="https://aus.social/tags/optimisation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>optimisation</span></a> <a href="https://aus.social/tags/AbductiveInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AbductiveInference</span></a></p>
Europe Says<p><a href="https://www.europesays.com/1881524/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">europesays.com/1881524/</span><span class="invisible"></span></a> Elon Musk Says There’s ‘Only a 20% Chance of Annihilation’ With AI <a href="https://pubeurope.com/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AI</span></a> <a href="https://pubeurope.com/tags/annihilation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>annihilation</span></a> <a href="https://pubeurope.com/tags/ArtificialIntelligence" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ArtificialIntelligence</span></a> <a href="https://pubeurope.com/tags/chance" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>chance</span></a> <a href="https://pubeurope.com/tags/Concern" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Concern</span></a> <a href="https://pubeurope.com/tags/ElonMusk" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ElonMusk</span></a> <a href="https://pubeurope.com/tags/GoodOutcome" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>GoodOutcome</span></a> <a href="https://pubeurope.com/tags/human" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>human</span></a> <a href="https://pubeurope.com/tags/HumanIntelligence" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>HumanIntelligence</span></a> <a href="https://pubeurope.com/tags/Interview" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Interview</span></a> <a href="https://pubeurope.com/tags/LastYear" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>LastYear</span></a> <a href="https://pubeurope.com/tags/NextYear" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>NextYear</span></a> <a href="https://pubeurope.com/tags/OpenAI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>OpenAI</span></a> <a href="https://pubeurope.com/tags/other" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>other</span></a> <a href="https://pubeurope.com/tags/PodcastEpisode" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PodcastEpisode</span></a> <a href="https://pubeurope.com/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a></p>
-0--1-<p>We <a href="https://mastodon.social/tags/Whisper" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Whisper</span></a> our <a href="https://mastodon.social/tags/SelfishWhispers" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SelfishWhispers</span></a> to our <a href="https://mastodon.social/tags/ImaginaryFriends" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ImaginaryFriends</span></a> to bend the laws of <a href="https://mastodon.social/tags/Physics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Physics</span></a> <a href="https://mastodon.social/tags/Chemistry" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Chemistry</span></a> <a href="https://mastodon.social/tags/Biology" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Biology</span></a> & <a href="https://mastodon.social/tags/Probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Probability</span></a> to grant our <a href="https://mastodon.social/tags/SelfishDesires" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SelfishDesires</span></a>. In <a href="https://mastodon.social/tags/ZombieJew" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ZombieJew</span></a>'s name <a href="https://mastodon.social/tags/WePray" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>WePray</span></a>. <a href="https://mastodon.social/tags/Amen" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Amen</span></a></p>
Sylvia Wenmackers 🦉🍀<p>The impact <a href="https://scholar.social/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> for <a href="https://scholar.social/tags/2024YR4" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>2024YR4</span></a> has been revised downward in the past days to well below 1%.</p><p>I wonder if showing how these impact probabilities change over time also helps to convince people that they should switch doors in the Monty Hall problem. 🚪🚪🐐</p><p>The analogy can be strengthened if you take into account that looking for the asteroid in a particular direction and *not* seeing it may also lower the impact probability. <a href="https://scholar.social/tags/PhilSci" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PhilSci</span></a></p><p>Animation from <a href="https://blogs.esa.int/rocketscience/2025/02/04/asteroid-2024-yr4-latest-updates/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">blogs.esa.int/rocketscience/20</span><span class="invisible">25/02/04/asteroid-2024-yr4-latest-updates/</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>
Sylvia Wenmackers 🦉🍀<p>Conference: Probability in Philosophy and Science at the University of Graz 🇦🇹, September 24-26. <br>It will be a long train journey, but I will speaking there; I don't think any of the other speakers are active on Mastodon.<br>🎯 More info on the event: <a href="https://philevents.org/event/show/131510" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">philevents.org/event/show/1315</span><span class="invisible">10</span></a><br>🎲 Call for papers (deadline April 30): <a href="https://philevents.org/event/show/131514" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">philevents.org/event/show/1315</span><span class="invisible">14</span></a><br><a href="https://scholar.social/tags/PhilSci" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PhilSci</span></a> <a href="https://scholar.social/tags/QuantumPhysics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>QuantumPhysics</span></a> <a href="https://scholar.social/tags/Probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Probability</span></a> <a href="https://scholar.social/tags/Epistemology" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Epistemology</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>
🐜🦅<p>My long-in-preparation Cambridge Element ‘Probability and Inductive Logic’ is now available. </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>Abstract: Reasoning from inconclusive evidence, or 'induction', is central to science and any applications we make of it. For that reason alone it demands the attention of philosophers of science. This element explores the prospects of using probability theory to provide an inductive logic: a framework for representing evidential support. Constraints on the ideal evaluation of hypotheses suggest that the overall standing of a hypothesis is represented by its probability in light of the total evidence, and incremental support, or confirmation, indicated by the hypothesis having a higher probability conditional on some evidence than it does unconditionally. This proposal is shown to have the capacity to reconstruct many canons of the scientific method and inductive inference. Along the way, significant objections are discussed, such as the challenge of inductive scepticism, and the objection that the probabilistic approach makes evidential support arbitrary. </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/induction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>induction</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/philosophy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>philosophy</span></a></p>
Sylvia Wenmackers 🦉🍀<p>I read about the 1-in-83 (>1% !) odds of a decent-sized <a href="https://scholar.social/tags/asteroid" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>asteroid</span></a> (2024 YR4) hitting Earth in 2032. ☄️ <a href="https://www.space.com/180-foot-asteroid-1-in-83-chance-hitting-Earth-2032" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">space.com/180-foot-asteroid-1-</span><span class="invisible">in-83-chance-hitting-Earth-2032</span></a><br>First thought: "Not now, large space rock." 😬<br>But soon after, I wondered: how do they determine this <a href="https://scholar.social/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a>? 🤔</p><p>Turns out it's a bit like weather forecasts: they run multiple simulations (variations on the measured data) and report the fraction of how often a certain event happens (rain/collision course). <a href="https://scholar.social/tags/2024yr4" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>2024yr4</span></a> 1/2</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>
ExploreLive feeds
Mastodon is the best way to keep up with what's happening.
Follow anyone across the fediverse and see it all in chronological order. No algorithms, ads, or clickbait in sight.
Create accountLoginDrag & drop to upload