#bayes — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #bayes, aggregated by home.social.
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Cell migration is fundamental to many biological processes (cancer metastasis, cellular immunity, development, ...). Here we introduce a new computational method & free tool to evaluate high-throughput cell migration assays.
#cancer #cellmigration #Bayes #quantitativebiology
https://doi.org/10.1371/journal.pcbi.1014472 -
Cell migration is fundamental to many biological processes (cancer metastasis, cellular immunity, development, ...). Here we introduce a new computational method & free tool to evaluate high-throughput cell migration assays.
#cancer #cellmigration #Bayes #quantitativebiology
https://doi.org/10.1371/journal.pcbi.1014472 -
How a computer reads text - from counting words to vectors
From tokenization through TF-IDF and Markov chains, to Word2Vec. How a computer turns text into numb...
https://gruszka.dev/en/how-computer-reads-text.html
#llm #ai #nlp #tokenization #word2vec #embeddings #tfidf #markov #bayes #languagemodels -
How a computer reads text - from counting words to vectors
From tokenization through TF-IDF and Markov chains, to Word2Vec. How a computer turns text into numb...
https://gruszka.dev/en/how-computer-reads-text.html
#llm #ai #nlp #tokenization #word2vec #embeddings #tfidf #markov #bayes #languagemodels -
Alright, future engineers!
**Conditional Probability:** P(A|B) is the prob of event A, given event B has already happened.
Ex: P(A|B) = P(A & B) / P(B). Think: Prob of engine failure *given* low oil pressure.
Pro-Tip: Essential for Bayesian inference & diagnostics!
#Probability #Bayes #STEM #StudyNotes -
Jak komputer czyta tekst - od liczenia słów do wektorów
Od tokenizacji przez TF-IDF i łańcuchy Markowa, aż po Word2Vec. Jak komputer zamienia tekst w liczby...
https://gruszka.dev/jak-komputer-czyta-tekst.html
#llm #ai #nlp #tokenizacja #word2vec #embeddings #tfidf #markow #bayes #languagemodels -
Jak komputer czyta tekst - od liczenia słów do wektorów
Od tokenizacji przez TF-IDF i łańcuchy Markowa, aż po Word2Vec. Jak komputer zamienia tekst w liczby...
https://gruszka.dev/jak-komputer-czyta-tekst.html
#llm #ai #nlp #tokenizacja #word2vec #embeddings #tfidf #markow #bayes #languagemodels -
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Hehehehe, we got another reviewer confused by our use of a 89% credible interval.
Cue the beauty of prime numbers! And it is my co-author's birth year, I am so happy that I can put this in the answer 😅! -
Hehehehe, we got another reviewer confused by our use of a 89% credible interval.
Cue the beauty of prime numbers! And it is my co-author's birth year, I am so happy that I can put this in the answer 😅! -
Monument du #cinéma #français, la #comédienne #NathalieBaye est morte
#RIP Nathalie #Bayes
Nous nous souviendrons de toi, de ta gentillesse, et de ton humanismehttps://www.france24.com/fr/france/20260418-actrice-nathalie-baye-est-morte-cannes-laura-smet-cinema
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Monument du #cinéma #français, la #comédienne #NathalieBaye est morte
#RIP Nathalie #Bayes
Nous nous souviendrons de toi, de ta gentillesse, et de ton humanismehttps://www.france24.com/fr/france/20260418-actrice-nathalie-baye-est-morte-cannes-laura-smet-cinema
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Returning to Bayesian computation now after some time away, I was delighted to see active work on JAGS 5.0!
https://sourceforge.net/projects/mcmc-jags/
#Bayes #JAGS -
Returning to Bayesian computation now after some time away, I was delighted to see active work on JAGS 5.0!
https://sourceforge.net/projects/mcmc-jags/
#Bayes #JAGS -
🤔 Ah, yet another "innovative" tool promising to fix your #non-deterministic #bugs by throwing #Bayes at #Git like it's some kind of magic wand. 🔮 Because clearly, what we all need in our #debugging toolbox is more statistical hand-waving and fewer #practical #solutions. 😂
https://github.com/hauntsaninja/git_bayesect #innovative #tools #HackerNews #ngated -
🤔 Ah, yet another "innovative" tool promising to fix your #non-deterministic #bugs by throwing #Bayes at #Git like it's some kind of magic wand. 🔮 Because clearly, what we all need in our #debugging toolbox is more statistical hand-waving and fewer #practical #solutions. 😂
https://github.com/hauntsaninja/git_bayesect #innovative #tools #HackerNews #ngated -
You're probably familiar with git bisect, which lets you find a commit that introduces a change in behavior via binary search (`git bisect`). But what if the change in behavior is non-deterministic?
`git bayesect` is a generalization of git bisect that uses Bayesian inference to solve this problem. If your code has started gaslighting you, give it a try!
https://hauntsaninja.github.io/git_bayesect.html #git #bayes
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You're probably familiar with git bisect, which lets you find a commit that introduces a change in behavior via binary search (`git bisect`). But what if the change in behavior is non-deterministic?
`git bayesect` is a generalization of git bisect that uses Bayesian inference to solve this problem. If your code has started gaslighting you, give it a try!
https://hauntsaninja.github.io/git_bayesect.html #git #bayes
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🖤💙 Oh, how nice! The International Labour Organization provided the recording of my yesterday's seminar on my new forecasting system for labour market outcomes and my R package bpvars I developed for them! It's all very good 🤍
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🖤💙 Oh, how nice! The International Labour Organization provided the recording of my yesterday's seminar on my new forecasting system for labour market outcomes and my R package bpvars I developed for them! It's all very good 🤍
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My friend Jay Wren recommended a book yesterday at lunch:
"Thinking, Fast and Slow"
by Daniel Kahnemanhttps://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman
A recommendation from Jay is an insta-buy. I got it as an audio-book (because of my commute).
It is not **at all** what I was expecting! I guess I thought maybe I was expecting something like a "self-help" book or the like. No. This is **not** a book aimed at a broad audience. This is a book aimed at people who understand (at least a bit about) probability and bias and category theory and ... What I'm saying is: it's not fluff. It's genuine knowledge aimed square at me. Jay's recommendation was on the money.
I wouldn't hand this to my MIL (it's past her at this point). I wouldn't hand it to my wife (she's certainly smart enough, but I don't think it falls in her circle of interest). I absolutely **would** recommend it to **you**, or to anyone in my circle of friends.
Go have some fun!
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UPDATE: THIS DID NOT CONSISTENTLY WORK--some model objects still corrupted.
Went through some agony learning saveRDS() does not preserve all parts of a cmdstan_model object. The model information is corrupted upon re-importing it with readRDS(). I have to use the qs_save() from the qs2 📦 for a save that preserves the model info. #rstats #bayes #mcmc
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UPDATE: THIS DID NOT CONSISTENTLY WORK--some model objects still corrupted.
Went through some agony learning saveRDS() does not preserve all parts of a cmdstan_model object. The model information is corrupted upon re-importing it with readRDS(). I have to use the qs_save() from the qs2 📦 for a save that preserves the model info. #rstats #bayes #mcmc
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BREAKING: Henceforth, I shall be using a new technique, “division”, to determine how much an item costs per count or unit weight.
This advance is a radical and exciting departure from the current method, counting on my fingers and toes.
🤦🏼 🤦🏼 🤦🏼
These f-ing morons.
https://www.instagram.com/reel/DTdoFw7kciA/?igsh=NTc4MTIwNjQ2YQ==
(For those unfamiliar, #Bayes was an 18th c. British #mathematician who developed an equation that has been used for centuries to calculate the probability of an event given what we already know. For example: if you get a medical test with 80% accuracy, and it returns positive, what is the chance you actually have the tested condition? Not 80%, as you may assume. The answer depends on the likelihood of developing that condition in the first place, and then applying the likelihood that the test is correct. Bayes’ theorem is pervasive in #medicine and many other disciplines, and not recently discovered by nut job RFK Jr. and his empty-headed lackeys.)
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BREAKING: Henceforth, I shall be using a new technique, “division”, to determine how much an item costs per count or unit weight.
This advance is a radical and exciting departure from the current method, counting on my fingers and toes.
🤦🏼 🤦🏼 🤦🏼
These f-ing morons.
https://www.instagram.com/reel/DTdoFw7kciA/?igsh=NTc4MTIwNjQ2YQ==
(For those unfamiliar, #Bayes was an 18th c. British #mathematician who developed an equation that has been used for centuries to calculate the probability of an event given what we already know. For example: if you get a medical test with 80% accuracy, and it returns positive, what is the chance you actually have the tested condition? Not 80%, as you may assume. The answer depends on the likelihood of developing that condition in the first place, and then applying the likelihood that the test is correct. Bayes’ theorem is pervasive in #medicine and many other disciplines, and not recently discovered by nut job RFK Jr. and his empty-headed lackeys.)
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The free open source programming language #Stan helps me to make sense of data and to quantify uncertainty. The next annual Stan conference (in Uppsala, Sweden) is now open for registration and abstract submissions. #bayes https://bayes.club/@mcmc_stan/115701059853138765
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The free open source programming language #Stan helps me to make sense of data and to quantify uncertainty. The next annual Stan conference (in Uppsala, Sweden) is now open for registration and abstract submissions. #bayes https://bayes.club/@mcmc_stan/115701059853138765
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When I was in high school, my driver’s ed teacher told us about when **he** learned to drive. He was all over the road. His father was teaching him and immediately saw why: the son was looking at the lines to stay between them, the lines he was touching, the close ones. The father told him "you have to look where you’re going … way down the road!"
I think they were **both** right (and of course, as always, this is not really about driving). You must keep aiming for the end goal, but you will also make constant adjustments (usually small) to get there. Both matter. This is how you solve problems with code. This is also Bayesian thinking.
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When I was in high school, my driver’s ed teacher told us about when **he** learned to drive. He was all over the road. His father was teaching him and immediately saw why: the son was looking at the lines to stay between them, the lines he was touching, the close ones. The father told him "you have to look where you’re going … way down the road!"
I think they were **both** right (and of course, as always, this is not really about driving). You must keep aiming for the end goal, but you will also make constant adjustments (usually small) to get there. Both matter. This is how you solve problems with code. This is also Bayesian thinking.
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Is a tossed coin/die actually random, or is it influenced by the initial physical forces that are imparted to it? Is the outcome an indication of your state at the time of the toss, and is any interpretation of that state valid? What is the probability it is?
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Is a tossed coin/die actually random, or is it influenced by the initial physical forces that are imparted to it? Is the outcome an indication of your state at the time of the toss, and is any interpretation of that state valid? What is the probability it is?
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When I was a child, I thought the world had things that were true and things that were false, i.e., things were "black and white".
Things happened to me, including reading "Gödel, Escher, Bach: an Eternal Golden Braid" #Godel #GodelEscherBach, and I realized "Oh! There’s a gray area! (and not only that, the very edges of the gray area are fuzzy!"
And then I learned about #Bayes (and #Laplace) and realized: "Oh shit! It’s **all** gray!"
It feels like you **know** some things to be true because have assigned them such high probabilities. So high, they seem certain. Sorry. It’s not actually 1. And always remember: probability is what you **know**; reality is outside of that (just like "is your blue the same as my blue?"). Yes! Your model is good enough to navigate the world and make good decisions; but absolutely don’t confuse that with having no room left to learn.
I know I said this in a weird way, but keep growing.
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When I was a child, I thought the world had things that were true and things that were false, i.e., things were "black and white".
Things happened to me, including reading "Gödel, Escher, Bach: an Eternal Golden Braid" #Godel #GodelEscherBach, and I realized "Oh! There’s a gray area! (and not only that, the very edges of the gray area are fuzzy!"
And then I learned about #Bayes (and #Laplace) and realized: "Oh shit! It’s **all** gray!"
It feels like you **know** some things to be true because have assigned them such high probabilities. So high, they seem certain. Sorry. It’s not actually 1. And always remember: probability is what you **know**; reality is outside of that (just like "is your blue the same as my blue?"). Yes! Your model is good enough to navigate the world and make good decisions; but absolutely don’t confuse that with having no room left to learn.
I know I said this in a weird way, but keep growing.
-
When I was a child, I thought the world had things that were true and things that were false, i.e., things were "black and white".
Things happened to me, including reading "Gödel, Escher, Bach: an Eternal Golden Braid" #Godel #GodelEscherBach, and I realized "Oh! There’s a gray area! (and not only that, the very edges of the gray area are fuzzy!"
And then I learned about #Bayes (and #Laplace) and realized: "Oh shit! It’s **all** gray!"
It feels like you **know** some things to be true because have assigned them such high probabilities. So high, they seem certain. Sorry. It’s not actually 1. And always remember: probability is what you **know**; reality is outside of that (just like "is your blue the same as my blue?"). Yes! Your model is good enough to navigate the world and make good decisions; but absolutely don’t confuse that with having no room left to learn.
I know I said this in a weird way, but keep growing.
-
When I was a child, I thought the world had things that were true and things that were false, i.e., things were "black and white".
Things happened to me, including reading "Gödel, Escher, Bach: an Eternal Golden Braid" #Godel #GodelEscherBach, and I realized "Oh! There’s a gray area! (and not only that, the very edges of the gray area are fuzzy!"
And then I learned about #Bayes (and #Laplace) and realized: "Oh shit! It’s **all** gray!"
It feels like you **know** some things to be true because have assigned them such high probabilities. So high, they seem certain. Sorry. It’s not actually 1. And always remember: probability is what you **know**; reality is outside of that (just like "is your blue the same as my blue?"). Yes! Your model is good enough to navigate the world and make good decisions; but absolutely don’t confuse that with having no room left to learn.
I know I said this in a weird way, but keep growing.
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There’s a strong urge to believe what you wish instead of what you can prove. Computer rumors are a great example. Many rumors have no basis other than being a feature someone wants. They call it "wish casting".
We want the world to be black and white. Some given statement is either true or false. But it’s not. Gödel #Godel describes at least three states: true, false, and unprovable (e.g., the statement "This statement is false". Can’t be true or false; it’s unprovable. Maybe there’s a better name.)
But it’s worse than that.
In science, a theory isn’t true … it’s just the best explanation we have so far. The whole endeavor of science is to keep finding better explanations. To make good decisions you don’t need the absolute best explanation, just one good enough to guide you to beneficial choices. (I said "prove" before, but to be more accurate I should be talking not about what you can prove, but about what you can’t disprove.)
#Bayes (really #Laplace) says a given notion isn’t true, it’s actually true-with-some-probability. Each new thing you observe impacts that #Probability. This is the actual math behind the #ScientificMethod. And it’s the truth of the world. Your beliefs must adapt to your observations, constantly, forever.
If you have unshakable faith in some set of "facts", you’re probably doing it wrong. Even when you’re right, you could be righter.
Of course, if you don’t adjust your beliefs with new input, if you don’t test, if you have "facts" instead of "very probable theories". If you believe things because of how strongly the person who convinced you believed instead of what they could actually show you. If you believe simply because that’s what your parents taught you. Then, well, you **might** be right (even a stopped clock is right twice a day). But at best you’re not going to make good decisions for yourself, and at worst you’re going to try to tell others what to do based on an inaccurate understanding.
It’s messy; and that’s just how it is.
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There’s a strong urge to believe what you wish instead of what you can prove. Computer rumors are a great example. Many rumors have no basis other than being a feature someone wants. They call it "wish casting".
We want the world to be black and white. Some given statement is either true or false. But it’s not. Gödel #Godel describes at least three states: true, false, and unprovable (e.g., the statement "This statement is false". Can’t be true or false; it’s unprovable. Maybe there’s a better name.)
But it’s worse than that.
In science, a theory isn’t true … it’s just the best explanation we have so far. The whole endeavor of science is to keep finding better explanations. To make good decisions you don’t need the absolute best explanation, just one good enough to guide you to beneficial choices. (I said "prove" before, but to be more accurate I should be talking not about what you can prove, but about what you can’t disprove.)
#Bayes (really #Laplace) says a given notion isn’t true, it’s actually true-with-some-probability. Each new thing you observe impacts that #Probability. This is the actual math behind the #ScientificMethod. And it’s the truth of the world. Your beliefs must adapt to your observations, constantly, forever.
If you have unshakable faith in some set of "facts", you’re probably doing it wrong. Even when you’re right, you could be righter.
Of course, if you don’t adjust your beliefs with new input, if you don’t test, if you have "facts" instead of "very probable theories". If you believe things because of how strongly the person who convinced you believed instead of what they could actually show you. If you believe simply because that’s what your parents taught you. Then, well, you **might** be right (even a stopped clock is right twice a day). But at best you’re not going to make good decisions for yourself, and at worst you’re going to try to tell others what to do based on an inaccurate understanding.
It’s messy; and that’s just how it is.
-
There’s a strong urge to believe what you wish instead of what you can prove. Computer rumors are a great example. Many rumors have no basis other than being a feature someone wants. They call it "wish casting".
We want the world to be black and white. Some given statement is either true or false. But it’s not. Gödel #Godel describes at least three states: true, false, and unprovable (e.g., the statement "This statement is false". Can’t be true or false; it’s unprovable. Maybe there’s a better name.)
But it’s worse than that.
In science, a theory isn’t true … it’s just the best explanation we have so far. The whole endeavor of science is to keep finding better explanations. To make good decisions you don’t need the absolute best explanation, just one good enough to guide you to beneficial choices. (I said "prove" before, but to be more accurate I should be talking not about what you can prove, but about what you can’t disprove.)
#Bayes (really #Laplace) says a given notion isn’t true, it’s actually true-with-some-probability. Each new thing you observe impacts that #Probability. This is the actual math behind the #ScientificMethod. And it’s the truth of the world. Your beliefs must adapt to your observations, constantly, forever.
If you have unshakable faith in some set of "facts", you’re probably doing it wrong. Even when you’re right, you could be righter.
Of course, if you don’t adjust your beliefs with new input, if you don’t test, if you have "facts" instead of "very probable theories". If you believe things because of how strongly the person who convinced you believed instead of what they could actually show you. If you believe simply because that’s what your parents taught you. Then, well, you **might** be right (even a stopped clock is right twice a day). But at best you’re not going to make good decisions for yourself, and at worst you’re going to try to tell others what to do based on an inaccurate understanding.
It’s messy; and that’s just how it is.
-
There’s a strong urge to believe what you wish instead of what you can prove. Computer rumors are a great example. Many rumors have no basis other than being a feature someone wants. They call it "wish casting".
We want the world to be black and white. Some given statement is either true or false. But it’s not. Gödel #Godel describes at least three states: true, false, and unprovable (e.g., the statement "This statement is false". Can’t be true or false; it’s unprovable. Maybe there’s a better name.)
But it’s worse than that.
In science, a theory isn’t true … it’s just the best explanation we have so far. The whole endeavor of science is to keep finding better explanations. To make good decisions you don’t need the absolute best explanation, just one good enough to guide you to beneficial choices. (I said "prove" before, but to be more accurate I should be talking not about what you can prove, but about what you can’t disprove.)
#Bayes (really #Laplace) says a given notion isn’t true, it’s actually true-with-some-probability. Each new thing you observe impacts that #Probability. This is the actual math behind the #ScientificMethod. And it’s the truth of the world. Your beliefs must adapt to your observations, constantly, forever.
If you have unshakable faith in some set of "facts", you’re probably doing it wrong. Even when you’re right, you could be righter.
Of course, if you don’t adjust your beliefs with new input, if you don’t test, if you have "facts" instead of "very probable theories". If you believe things because of how strongly the person who convinced you believed instead of what they could actually show you. If you believe simply because that’s what your parents taught you. Then, well, you **might** be right (even a stopped clock is right twice a day). But at best you’re not going to make good decisions for yourself, and at worst you’re going to try to tell others what to do based on an inaccurate understanding.
It’s messy; and that’s just how it is.
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Excited to share my new article “Taken for granted? Investigating constructivist principles with Bayes’ theorem in Digital Humanities scholarship” in DSH! I explore how constructivist principles intersect (and sometimes clash) with Bayesian methods in DH.
https://doi.org/10.1093/llc/fqaf063
#DigitalHumanities #DHTheory #DH2023 #Epistemology #Bayes #OpenAccess
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Excited to share my new article “Taken for granted? Investigating constructivist principles with Bayes’ theorem in Digital Humanities scholarship” in DSH! I explore how constructivist principles intersect (and sometimes clash) with Bayesian methods in DH.
https://doi.org/10.1093/llc/fqaf063
#DigitalHumanities #DHTheory #DH2023 #Epistemology #Bayes #OpenAccess
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Excited to share my new article “Taken for granted? Investigating constructivist principles with Bayes’ theorem in Digital Humanities scholarship” in DSH! I explore how constructivist principles intersect (and sometimes clash) with Bayesian methods in DH.
https://doi.org/10.1093/llc/fqaf063
#DigitalHumanities #DHTheory #DH2023 #Epistemology #Bayes #OpenAccess
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Excited to share my new article “Taken for granted? Investigating constructivist principles with Bayes’ theorem in Digital Humanities scholarship” in DSH! I explore how constructivist principles intersect (and sometimes clash) with Bayesian methods in DH.
https://doi.org/10.1093/llc/fqaf063
#DigitalHumanities #DHTheory #DH2023 #Epistemology #Bayes #OpenAccess
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Immune escape of Hepatitis B virus (HBV): based on >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 #Bayesian HAMdetector method.
https://doi.org/10.1016/j.jhep.2024.10.047
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Immune escape of Hepatitis B virus (HBV): based on >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 #Bayesian HAMdetector method.
https://doi.org/10.1016/j.jhep.2024.10.047
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Immune escape of Hepatitis B virus (HBV): based on >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 #Bayesian HAMdetector method.
https://doi.org/10.1016/j.jhep.2024.10.047
