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#bayes

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Daniel Hoffmann 🥬<p>Detective work with <a href="https://mathstodon.xyz/tags/genomes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>genomes</span></a>: some invasive species have been introduced deliberately, others inadvertently. A new <a href="https://mathstodon.xyz/tags/statistical" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistical</span></a> framework helps to track what probably happened. Demonstration object is the Pacific oyster. <a href="https://mathstodon.xyz/tags/bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayes</span></a> <a href="https://www.pnas.org/doi/abs/10.1073/pnas.2418730122?af=R" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">pnas.org/doi/abs/10.1073/pnas.</span><span class="invisible">2418730122?af=R</span></a></p>
Ritesh Bhagwat<p>My Ram Navami Greetings with a Bayesian Twist !</p><p><a href="https://mastodon.social/tags/ramnavami" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ramnavami</span></a> <a href="https://mastodon.social/tags/bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayes</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>
Dr Mircea Zloteanu 🌼🐝<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statstab</span></a> #295 The Fallacy of the Null-Hypothesis Significance Test</p><p>Thoughts: "the [..] aim of a scientific experiment is not to precipitate decisions, but to make an appropriate adjustment in the degree to which one accepts, or believes, the hypothesis"</p><p><a href="https://mastodon.social/tags/NHST" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>NHST</span></a> <a href="https://mastodon.social/tags/Bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayes</span></a> <a href="https://mastodon.social/tags/ConfidenceIntervals" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ConfidenceIntervals</span></a> <a href="https://mastodon.social/tags/pvalues" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pvalues</span></a> <a href="https://mastodon.social/tags/significance" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>significance</span></a> <a href="https://mastodon.social/tags/testing" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>testing</span></a> <a href="https://mastodon.social/tags/hypotheses" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>hypotheses</span></a> <a href="https://mastodon.social/tags/likelihood" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>likelihood</span></a> <a href="https://mastodon.social/tags/critique" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>critique</span></a> <a href="https://mastodon.social/tags/fallacy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>fallacy</span></a></p><p><a href="http://stats.org.uk/statistical-inference/Rozeboom1960.pdf" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">http://</span><span class="ellipsis">stats.org.uk/statistical-infer</span><span class="invisible">ence/Rozeboom1960.pdf</span></a></p>
Dr Mircea Zloteanu 🌼🐝<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statstab</span></a> #293 The Bayesian Bootstrap</p><p>Thoughts: I need to think more on where bootstrapping makes sense in a bayesian setting. But here's a tutorial.</p><p><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/bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayesian</span></a> <a href="https://mastodon.social/tags/bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayes</span></a> <a href="https://mastodon.social/tags/bootstrap" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bootstrap</span></a> <a href="https://mastodon.social/tags/resampling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>resampling</span></a> </p><p><a href="https://towardsdatascience.com/the-bayesian-bootstrap-6ca4a1d45148/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">towardsdatascience.com/the-bay</span><span class="invisible">esian-bootstrap-6ca4a1d45148/</span></a></p>
Dr Mircea Zloteanu 🌼🐝<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statstab</span></a> #279 Diagnosing the Misuse of the Bayes Factor in Applied Research</p><p>Thoughts: As with NHST, Null Hypothesis Bayesian Testing (NHBT) can also be easily misunderstood.</p><p><a href="https://mastodon.social/tags/bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayes</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/NHBT" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>NHBT</span></a> <a href="https://mastodon.social/tags/misuse" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>misuse</span></a> <a href="https://mastodon.social/tags/QRPs" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>QRPs</span></a> <a href="https://mastodon.social/tags/error" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>error</span></a></p><p><a href="https://journals.sagepub.com/doi/10.1177/25152459231213371" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">journals.sagepub.com/doi/10.11</span><span class="invisible">77/25152459231213371</span></a></p>
Dr Mircea Zloteanu 🌼🐝<p><a href="https://mastodon.social/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> help: is there any online tutorial for ordinal CFA with {blavaan}?</p><p><a href="https://mastodon.social/tags/bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayes</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/blavaan" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>blavaan</span></a> <a href="https://mastodon.social/tags/lavaan" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>lavaan</span></a> <a href="https://mastodon.social/tags/cfa" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>cfa</span></a> <a href="https://mastodon.social/tags/sem" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>sem</span></a> <a href="https://mastodon.social/tags/tutorial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>tutorial</span></a></p>
Dr Mircea Zloteanu 🌼🐝<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statstab</span></a> #272 Different meanings of p-values</p><p>Thoughts: A riveting (&amp; confusing) discussion on the definitions &amp; properties of p-values. W/ guest appearance from some big names in stats, from all camps.</p><p><a href="https://mastodon.social/tags/NHST" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>NHST</span></a> <a href="https://mastodon.social/tags/pvalues" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pvalues</span></a> <a href="https://mastodon.social/tags/divergence" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>divergence</span></a> <a href="https://mastodon.social/tags/bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayes</span></a> <a href="https://mastodon.social/tags/compatibility" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>compatibility</span></a></p><p><a href="https://statmodeling.stat.columbia.edu/2023/04/14/4-different-meanings-of-p-value-and-how-my-thinking-has-changed/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statmodeling.stat.columbia.edu</span><span class="invisible">/2023/04/14/4-different-meanings-of-p-value-and-how-my-thinking-has-changed/</span></a></p>
pglpm<p><span class="h-card" translate="no"><a href="https://mastodon.social/@bthalpin" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>bthalpin</span></a></span> <br>Curiosity: what does it do if you ask for an 89% *credibility* interval, maybe even asking not to make distributional assumptions?</p><p><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/deepseek" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>deepseek</span></a> <a href="https://c.im/tags/llm" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>llm</span></a></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>
Dr Mircea Zloteanu 🌼🐝<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statstab</span></a> #232 Bayesian Interval-Null Testing</p><p>Thoughts: @JASPStats has a module for Equivalence Tests that include Bayesian Overlapping and Non-Overlapping Hypothesis Testing.</p><p><a href="https://mastodon.social/tags/equivalencetests" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>equivalencetests</span></a> <a href="https://mastodon.social/tags/bayesfactors" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayesfactors</span></a> <a href="https://mastodon.social/tags/jasp" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>jasp</span></a> <a href="https://mastodon.social/tags/noeffect" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>noeffect</span></a> <a href="https://mastodon.social/tags/bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayes</span></a><br> <a href="https://jasp-stats.org/2020/06/02/frequentist-and-bayesian-equivalence-testing-in-jasp/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">jasp-stats.org/2020/06/02/freq</span><span class="invisible">uentist-and-bayesian-equivalence-testing-in-jasp/</span></a></p>
Ulrike Hahn<p>„calling something logic doesn’t make it so. Calling someone rational doesn’t make it so“ </p><p>I’ve been thinking for a while that, as someone who works on human rationality and rational argument, I should write a blog post on what that actually means (and, maybe more importantly, doesn‘t mean).</p><p>in the meantime, though, I found much to agree with in this piece: </p><p>Title: The magical thinking of guys who love logic <br><a href="https://theoutline.com/post/7083/the-magical-thinking-of-guys-who-love-logic" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">theoutline.com/post/7083/the-m</span><span class="invisible">agical-thinking-of-guys-who-love-logic</span></a> </p><p><a href="https://fediscience.org/tags/logic" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>logic</span></a> <a href="https://fediscience.org/tags/rationality" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>rationality</span></a> <a href="https://fediscience.org/tags/argument" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>argument</span></a> <a href="https://fediscience.org/tags/Bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayes</span></a></p>
Eric Schares<p>Adventures in writing a simple <br><a href="https://scholar.social/tags/Bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayes</span></a> model! I have a question on why I'm getting a bi-modal joint posterior (alpha~sigma) when I just model a Normal distribution up on Cross Validated.</p><p>Would welcome any <a href="https://scholar.social/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> input or advice.</p><p><a href="https://stats.stackexchange.com/questions/657633/bi-modal-mcmc-joint-posterior-when-modeling-normal-distribution" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">stats.stackexchange.com/questi</span><span class="invisible">ons/657633/bi-modal-mcmc-joint-posterior-when-modeling-normal-distribution</span></a></p>
Dr Mircea Zloteanu 🌼🐝<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statstab</span></a> #229 Prior Modeling<br>by <span class="h-card" translate="no"><a href="https://fediscience.org/@betanalpha" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>betanalpha</span></a></span> </p><p>Thoughts: Thorough overview of the prior elicitation process and ways to think about priors.</p><p><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/priors" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>priors</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/metascience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>metascience</span></a> <a href="https://mastodon.social/tags/bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayes</span></a> <a href="https://mastodon.social/tags/bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayesian</span></a></p><p><a href="https://betanalpha.github.io/assets/case_studies/prior_modeling.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">betanalpha.github.io/assets/ca</span><span class="invisible">se_studies/prior_modeling.html</span></a></p>
Dr Mircea Zloteanu 🌼🐝<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statstab</span></a> #228 Applied Modelling in Drug Development - Setting priors in {brms}</p><p>Thoughts: Part of a larger book, useful bit for understanding how to set priors &amp; check them for bayesian models &amp; meta-analyses</p><p><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/brms" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>brms</span></a> <a href="https://mastodon.social/tags/priors" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>priors</span></a> <a href="https://mastodon.social/tags/metaanalysis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>metaanalysis</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/r" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>r</span></a> <a href="https://mastodon.social/tags/bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayes</span></a> <a href="https://mastodon.social/tags/drugs" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>drugs</span></a> <a href="https://mastodon.social/tags/clinicaltrials" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>clinicaltrials</span></a> </p><p><a href="https://opensource.nibr.com/bamdd/src/01c_priors.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">opensource.nibr.com/bamdd/src/</span><span class="invisible">01c_priors.html</span></a></p>
Dr Mircea Zloteanu 🌼🐝<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statstab</span></a> #227 Parameterization of Response Distributions in {brms}</p><p>Thoughts: If you use <a href="https://mastodon.social/tags/brms" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>brms</span></a> and can read mathematical notation (who can't, right?), this page will be useful.</p><p><a href="https://mastodon.social/tags/r" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>r</span></a> <a href="https://mastodon.social/tags/bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayes</span></a> <a href="https://mastodon.social/tags/models" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>models</span></a> <a href="https://mastodon.social/tags/distributions" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>distributions</span></a> <a href="https://mastodon.social/tags/likelihood" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>likelihood</span></a> <a href="https://mastodon.social/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a></p><p><a href="https://cran.r-project.org/web/packages/brms/vignettes/brms_families.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">cran.r-project.org/web/package</span><span class="invisible">s/brms/vignettes/brms_families.html</span></a></p>
Daniel Hoffmann 🥬<p>Immune escape of Hepatitis B virus (HBV): based on &gt;500 HBV genomes and clinical data we could discover many mutations by which the virus can escape immune recognition. We also show how the adaptation of HBV to the immune system changes over the course of the infection. The key tool in this collaboration of virologists and bioinformaticians was our <a href="https://mathstodon.xyz/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> HAMdetector method.</p><p><a href="https://doi.org/10.1016/j.jhep.2024.10.047" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.1016/j.jhep.2024.10</span><span class="invisible">.047</span></a></p><p><a href="https://github.com/HAMdetector/Escape.jl" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/HAMdetector/Escape.</span><span class="invisible">jl</span></a></p><p><a href="https://mathstodon.xyz/tags/infections" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>infections</span></a> <a href="https://mathstodon.xyz/tags/HBV" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>HBV</span></a> <a href="https://mathstodon.xyz/tags/immunity" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>immunity</span></a> <a href="https://mathstodon.xyz/tags/medicine" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>medicine</span></a> <a href="https://mathstodon.xyz/tags/Bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayes</span></a> <a href="https://mathstodon.xyz/tags/HAMdetector" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>HAMdetector</span></a></p>
Dr Mircea Zloteanu 🌼🐝<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statstab</span></a> #217 The distribution of p-values obtained in replications depends only on the original p-value. How can it be true?</p><p>Thoughts: A great discussion where the author <span class="h-card" translate="no"><a href="https://nerdculture.de/@thenewstats" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>thenewstats</span></a></span> chimes in to explain the issue.</p><p><a href="https://mastodon.social/tags/replication" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>replication</span></a> <a href="https://mastodon.social/tags/pvalues" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pvalues</span></a> <a href="https://mastodon.social/tags/confidenceinvervals" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>confidenceinvervals</span></a> <a href="https://mastodon.social/tags/bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayes</span></a></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" §&nbsp;VII.7.2 (emphasis in the original) &lt;<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>&gt;.</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>
Dr Mircea Zloteanu 🌼🐝<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statstab</span></a> #196 JASP Bayesian ANOVA</p><p>Thoughts: @JASPStats is used by researchers to "add some bayes factors" to their results. But, do you know what those actually reflect? Here is what their team says:</p><p><a href="https://mastodon.social/tags/bayes" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayes</span></a> <a href="https://mastodon.social/tags/bayesfactors" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bayesfactors</span></a> <a href="https://mastodon.social/tags/anova" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>anova</span></a> <a href="https://mastodon.social/tags/modelcomparison" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modelcomparison</span></a></p><p><a href="https://static.jasp-stats.org/about-bayesian-anova.html" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">static.jasp-stats.org/about-ba</span><span class="invisible">yesian-anova.html</span></a></p>
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