#statstab #319 Small Sample Size Solutions [book]
Thoughts: This should just be the default text for psychologists, as most research fits the "small sample" label.
#smallsample #book #guide #bayesian #permutation #sem #metaanalysis #nof1 #missingdata
It's important to emphasize that "realistic-looking" data does *not* mean "realistic" data – especially high-dimensional data (unfortunately that post doesn't warn against this).
If one had an algorithm that generated realistic data for a given inference problem, it would mean that that inference problem had been solved. So: for educational purposes, why not. But for validation-like purposes, use with uttermost caution and at your own peril.
The bayesEO package provides a Bayesian approach to post-processing ML-generated images. It refines class probabilities, removes outliers, and improves labeling for more accurate classification.
Байесовская собака: анализ пёсьего компаса
Ориентируются ли собаки по компасу, когда делают свои грязные дела? Оказывается — да! Если вам интересно, как можно это подтвердить в домашних условиях, используя компас, Байесовскую статистику и собаку (собака не включена), то добро пожаловать под кат.
"Bayesian Meta-Analysis: A Practical Introduction", the new book by Robert Grant and Gian Luca Di Tanna, will be out in June on CRC Press! Here is the text from the back cover to give you an overview. #Bayesian
Happy Birthday, Laplace! 
"One sees in this essay that the theory of probabilities is basically only common sense reduced to a calculus. It makes one estimate accurately what right-minded people feel by a sort of instinct, often without being able to give a reason for it. It leaves nothing arbitrary in the choice of opinions and of making up one's mind, every time one is able, by this means, to determine the most advantageous choice. Thereby, it becomes the most happy supplement to ignorance and to the weakness of the human mind. If one considers the analytical methods to which this theory has given rise, the truth of the principles that serve as the groundwork, the subtle and delicate logic needed to use them in the solution of the problems, the public-benefit businesses that depend on it, and the extension that it has received and may still receive from its application to the most important questions of natural philosophy and the moral sciences; if one observes also that even in matters which cannot be handled by the calculus, it gives the best rough estimates to guide us in our judgements, and that it teaches us to guard ourselves from the illusions which often mislead us, one will see that there is no science at all more worthy of our consideration, and that it would be a most useful part of the system of public education."
*Philosophical Essay on Probabilities*, 1814 <https://doi.org/10.1007/978-1-4612-4184-3>
We're delighted to present the cover of the book "Bayesian Meta-Analysis: a practical introduction", by Robert Grant and Gian Luca Di Tanna, due out on CRC Press, June 2025.
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funniest accidental death poll, 2024, boosts welcome
what's your funniest accidental death of 2024 #2024Poll #poll
Shipping tycoon #AngelaChao drink driving her #Tesla into a pond and drowning? 
Tech entrepreneur #MikeLynch being accidentally assassinated by #HP when his 56m yacht the #Bayesian sank off the coast of Sicily?
#BrianThompson the late healthcare CEO who accidentally intercepted a mysterious bullet fired probably from a #Welrod or #StationSIX9 
Billionaire founder of high street fashion chain #Mango #IsakAndic who fell into a ravine
https://learnbayesstats.com/episodes/8f372809-3905-4110-8e1b-2f5ca1f95b33
New book on Bayesian inference and human cognition. I have always enjoyed material from Tom Griffiths and also from Josh Tenenbaum, and I expect this new collected chapters would also be excellent. If you want to explore more literature, the contributing authors of individual chapters are also wonderful.
"A p-value is an #estimate of p(Data | Null Hypothesis). " – not correct. A p-value is an estimate of
p(Data or other imagined data | Null Hypothesis)
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.
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.
With this stopping rule, your p-value will depend on the probability that you get the grant. This is not a joke.
"*What the use of P implies, therefore, is that a hypothesis that may be true may be rejected because it has not predicted observable results that have not occurred.* This seems a remarkable procedure. On the face of it the fact that such results have not occurred might more reasonably be taken as evidence for the law, not against it." – H. Jeffreys, "Theory of Probability" § VII.7.2 (emphasis in the original) <https://doi.org/10.1093/oso/9780198503682.001.0001>.
@paninid p-values, to a large extent, exist because calculating the posterior is computationally expensive. Not all fields use the .05 cutoff.
A p-value is an #estimate of p(Data | Null Hypothesis). If the two #hypotheses are equally likely and they are mutually exclusive and they are closed over the #hypothesis space, then this is the same as p(Hypothesis | Data).
Meaning, under certain assumption, the p-value does represent the actually probability of being wrong.
However, given modern computers, there is no reason that #Bayesian odds-ratios can't completely replace their usage and avoid the many many problems with p-values.
Do you use Probability Density Function (PDF) plots to show your results?
Discover how compressing PDFs impacts data interpretation in research!
Racquel Fygenson reveals surprising insights from our study on plot scaling. 
Video: https://www.youtube.com/watch?v=BEsD1RIIjCo&ab_channel=Dr.LacePadilla
Paper: https://vis.khoury.northeastern.edu/pubs/Fygenson2024ImpactVerticalScaling/
Would you like to become a business data scientist? 
Small advertisement for my Ph.D. thesis and code, focused on #computervision for #robotics.
Using #julialang to implement #Bayesian inference algorithms for the 6D pose estimation of known objects in depth images.
TLDR: it works even with occlusions; needs <1sec on a GPU; does not need training; future research could focus on including color images / semantic information since SOA performs much better if color images are available.
doc: https://publications.rwth-aachen.de/record/985219
code: https://github.com/rwth-irt/BayesianPoseEstimation.jl
Nach Yachtunglück: Hewlett Packard fordert Milliarden von Lynchs Witwe
Der britische Tech-Tycoon Mike Lynch starb beim Untergang der Luxusjacht "Bayesian". Hewlett Packard Enterprise fordert dennoch weiter Schadenersatz in Milliardenhöhe. Haftet nun Lynchs Witwe? Von Angela Göpfert.
(1/3) 𝐁𝐨𝐨𝐤 𝐨𝐟 𝐭𝐡𝐞 𝐖𝐞𝐞𝐤 - 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐑𝐞𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 
I consider Statistical Rethinking by 𝐏𝐫𝐨𝐟. 𝐑𝐢𝐜𝐡𝐚𝐫𝐝 𝐌𝐜𝐄𝐥𝐫𝐞𝐚𝐭𝐡 to be one of the best resources for getting started with Bayesian Statistics 
#Bayesian #Karma #Arrogance: Stir, bring to a boil and letter RIP
https://apple.news/Ao1y34QTFRlC55gTkHL8qlQ
You're not Rich, you're not a human being worth giving a fuck about.
"HOW DARE YOU ALLOW A RICH MAN TO DIE! HOW DARE YOU!!!!"
They arrest people for helping refugees out the sea, but let a rich man die and suddenly human life has value.
