#statisticalmodels — Public Fediverse posts

hey WOW: my commentary was just published by PNAS!! the only thing i didn't feel competent to comment on was the statistical modeling (although it's very cool!) but all the other words are mine!

#Salmonella #StatisticalModels #WildBirds #microbiology #ornithology https://doi.org/10.1073/pnas.2537200123

hey WOW: my commentary was just published by PNAS!! the only thing i didn't feel competent to comment on was the statistical modeling (although it's very cool!) but all the other words are mine!

#Salmonella #StatisticalModels #WildBirds #microbiology #ornithology https://doi.org/10.1073/pnas.2537200123

hey WOW: my commentary was just published by PNAS!! the only thing i didn't feel competent to comment on was the statistical modeling (although it's very cool!) but all the other words are mine!

#Salmonella #StatisticalModels #WildBirds #microbiology #ornithology https://doi.org/10.1073/pnas.2537200123

hey WOW: my commentary was just published by PNAS!! the only thing i didn't feel competent to comment on was the statistical modeling (although it's very cool!) but all the other words are mine!

#Salmonella #StatisticalModels #WildBirds #microbiology #ornithology https://doi.org/10.1073/pnas.2537200123

hey WOW: my commentary was just published by PNAS!! the only thing i didn't feel competent to comment on was the statistical modeling (although it's very cool!) but all the other words are mine!

#Salmonella #StatisticalModels #WildBirds #microbiology #ornithology https://doi.org/10.1073/pnas.2537200123

hey WOW: my commentary was just published by PNAS!! the only thing i didn't feel competent to comment on was the statistical modeling (although it's very cool!) but all the other words are mine!

#Salmonella #StatisticalModels #WildBirds #microbiology #ornithology https://doi.org/10.1073/pnas.2537200123

hey WOW: my commentary was just published by PNAS!! the only thing i didn't feel competent to comment on was the statistical modeling (although it's very cool!) but all the other words are mine!

#Salmonella #StatisticalModels #WildBirds #microbiology #ornithology https://doi.org/10.1073/pnas.2537200123

hey WOW: my commentary was just published by PNAS!! the only thing i didn't feel competent to comment on was the statistical modeling (although it's very cool!) but all the other words are mine!

#Salmonella #StatisticalModels #WildBirds #microbiology #ornithology https://doi.org/10.1073/pnas.2537200123

🚨 Breaking News: Statistical Models Demand #JavaScript & #Cookies for Basic Functionality! 🙄 Apparently, even the dumbest algorithms have standards. Who knew data science needed a snack 🍪 and some code to function? 😂
https://statmodeling.stat.columbia.edu/2025/04/18/dumb-statistical-models-always-making-people-look-bad/#comments #BreakingNews #StatisticalModels #DataScience #HackerNews #ngated

So ChatGPT is basically just natural language programming where you don’t know what data and algorithms you’re working with and can’t predict the exact result you’re going to get.

Sure sounds like progress to me!

🤓👍

#ChatGPT #openAI #artificialIntelligence #ai #machineLearning #ml #largeLanguageModels #llm #statisticalModels #probabilisticModels #glorifiedMatrixMultiplication

So ChatGPT is basically just natural language programming where you don’t know what data and algorithms you’re working with and can’t predict the exact result you’re going to get.

Sure sounds like progress to me!

🤓👍

#ChatGPT #openAI #artificialIntelligence #ai #machineLearning #ml #largeLanguageModels #llm #statisticalModels #probabilisticModels #glorifiedMatrixMultiplication

So ChatGPT is basically just natural language programming where you don’t know what data and algorithms you’re working with and can’t predict the exact result you’re going to get.

Sure sounds like progress to me!

🤓👍

#ChatGPT #openAI #artificialIntelligence #ai #machineLearning #ml #largeLanguageModels #llm #statisticalModels #probabilisticModels #glorifiedMatrixMultiplication

So ChatGPT is basically just natural language programming where you don’t know what data and algorithms you’re working with and can’t predict the exact result you’re going to get.

Sure sounds like progress to me!

🤓👍

#ChatGPT #openAI #artificialIntelligence #ai #machineLearning #ml #largeLanguageModels #llm #statisticalModels #probabilisticModels #glorifiedMatrixMultiplication

So ChatGPT is basically just natural language programming where you don’t know what data and algorithms you’re working with and can’t predict the exact result you’re going to get.

Sure sounds like progress to me!

🤓👍

#ChatGPT #openAI #artificialIntelligence #ai #machineLearning #ml #largeLanguageModels #llm #statisticalModels #probabilisticModels #glorifiedMatrixMultiplication

.> The human mind is not, like ChatGPT and its ilk, a lumbering statistical engine for pattern matching, gorging on hundreds of terabytes of data and extrapolating the most likely conversational response or most probable answer to a scientific question. On the contrary, the human mind is a surprisingly efficient and even elegant system that operates with small amounts of information; it seeks not to infer brute correlations among data points but to create explanations...

.> Indeed, such programs are stuck in a prehuman or nonhuman phase of cognitive evolution. Their deepest flaw is the absence of the most critical capacity of any intelligence: to say not only what is the case, what was the case and what will be the case — that’s description and prediction — but also what is not the case and what could and could not be the case. Those are the ingredients of explanation, the mark of true intelligence.

.> The crux of machine learning is description and prediction; it does not posit any causal mechanisms or physical laws. Of course, any human-style explanation is not necessarily correct; we are fallible. But this is part of what it means to think: To be right, it must be possible to be wrong. Intelligence consists not only of creative conjectures but also of creative criticism. Human-style thought is based on possible explanations and error correction, a process that gradually limits what possibilities can be rationally considered...

.> But ChatGPT and similar programs are, by design, unlimited in what they can “learn” (which is to say, memorize); they are incapable of distinguishing the possible from the impossible. Unlike humans, for example, who are endowed with a universal grammar that limits the languages we can learn to those with a certain kind of almost mathematical elegance, these programs learn humanly possible and humanly impossible languages with equal facility.

.> But ChatGPT and similar programs are, by design, unlimited in what they can “learn” (which is to say, memorize); they are incapable of distinguishing the possible from the impossible. Unlike humans, for example, who are endowed with a universal grammar that limits the languages we can learn to those with a certain kind of almost mathematical elegance, these programs learn humanly possible and humanly impossible languages with equal facility.

.> Whereas humans are limited in the kinds of explanations we can rationally conjecture, machine learning systems can learn both that the earth is flat and that the earth is round. They trade merely in probabilities that change over time.

.> For this reason, the predictions of machine learning systems will always be superficial and dubious.

.> The human mind is not, like ChatGPT and its ilk, a lumbering statistical engine for pattern matching, gorging on hundreds of terabytes of data and extrapolating the most likely conversational response or most probable answer to a scientific question. On the contrary, the human mind is a surprisingly efficient and even elegant system that operates with small amounts of information; it seeks not to infer brute correlations among data points but to create explanations...

.> Indeed, such programs are stuck in a prehuman or nonhuman phase of cognitive evolution. Their deepest flaw is the absence of the most critical capacity of any intelligence: to say not only what is the case, what was the case and what will be the case — that’s description and prediction — but also what is not the case and what could and could not be the case. Those are the ingredients of explanation, the mark of true intelligence.

.> The crux of machine learning is description and prediction; it does not posit any causal mechanisms or physical laws. Of course, any human-style explanation is not necessarily correct; we are fallible. But this is part of what it means to think: To be right, it must be possible to be wrong. Intelligence consists not only of creative conjectures but also of creative criticism. Human-style thought is based on possible explanations and error correction, a process that gradually limits what possibilities can be rationally considered...

.> But ChatGPT and similar programs are, by design, unlimited in what they can “learn” (which is to say, memorize); they are incapable of distinguishing the possible from the impossible. Unlike humans, for example, who are endowed with a universal grammar that limits the languages we can learn to those with a certain kind of almost mathematical elegance, these programs learn humanly possible and humanly impossible languages with equal facility.

.> But ChatGPT and similar programs are, by design, unlimited in what they can “learn” (which is to say, memorize); they are incapable of distinguishing the possible from the impossible. Unlike humans, for example, who are endowed with a universal grammar that limits the languages we can learn to those with a certain kind of almost mathematical elegance, these programs learn humanly possible and humanly impossible languages with equal facility.

.> Whereas humans are limited in the kinds of explanations we can rationally conjecture, machine learning systems can learn both that the earth is flat and that the earth is round. They trade merely in probabilities that change over time.

.> For this reason, the predictions of machine learning systems will always be superficial and dubious.

.> The human mind is not, like ChatGPT and its ilk, a lumbering statistical engine for pattern matching, gorging on hundreds of terabytes of data and extrapolating the most likely conversational response or most probable answer to a scientific question. On the contrary, the human mind is a surprisingly efficient and even elegant system that operates with small amounts of information; it seeks not to infer brute correlations among data points but to create explanations...

.> Indeed, such programs are stuck in a prehuman or nonhuman phase of cognitive evolution. Their deepest flaw is the absence of the most critical capacity of any intelligence: to say not only what is the case, what was the case and what will be the case — that’s description and prediction — but also what is not the case and what could and could not be the case. Those are the ingredients of explanation, the mark of true intelligence.

.> The crux of machine learning is description and prediction; it does not posit any causal mechanisms or physical laws. Of course, any human-style explanation is not necessarily correct; we are fallible. But this is part of what it means to think: To be right, it must be possible to be wrong. Intelligence consists not only of creative conjectures but also of creative criticism. Human-style thought is based on possible explanations and error correction, a process that gradually limits what possibilities can be rationally considered...

.> But ChatGPT and similar programs are, by design, unlimited in what they can “learn” (which is to say, memorize); they are incapable of distinguishing the possible from the impossible. Unlike humans, for example, who are endowed with a universal grammar that limits the languages we can learn to those with a certain kind of almost mathematical elegance, these programs learn humanly possible and humanly impossible languages with equal facility.

.> But ChatGPT and similar programs are, by design, unlimited in what they can “learn” (which is to say, memorize); they are incapable of distinguishing the possible from the impossible. Unlike humans, for example, who are endowed with a universal grammar that limits the languages we can learn to those with a certain kind of almost mathematical elegance, these programs learn humanly possible and humanly impossible languages with equal facility.

.> Whereas humans are limited in the kinds of explanations we can rationally conjecture, machine learning systems can learn both that the earth is flat and that the earth is round. They trade merely in probabilities that change over time.

.> For this reason, the predictions of machine learning systems will always be superficial and dubious.

.> The human mind is not, like ChatGPT and its ilk, a lumbering statistical engine for pattern matching, gorging on hundreds of terabytes of data and extrapolating the most likely conversational response or most probable answer to a scientific question. On the contrary, the human mind is a surprisingly efficient and even elegant system that operates with small amounts of information; it seeks not to infer brute correlations among data points but to create explanations...

.> Indeed, such programs are stuck in a prehuman or nonhuman phase of cognitive evolution. Their deepest flaw is the absence of the most critical capacity of any intelligence: to say not only what is the case, what was the case and what will be the case — that’s description and prediction — but also what is not the case and what could and could not be the case. Those are the ingredients of explanation, the mark of true intelligence.

.> The crux of machine learning is description and prediction; it does not posit any causal mechanisms or physical laws. Of course, any human-style explanation is not necessarily correct; we are fallible. But this is part of what it means to think: To be right, it must be possible to be wrong. Intelligence consists not only of creative conjectures but also of creative criticism. Human-style thought is based on possible explanations and error correction, a process that gradually limits what possibilities can be rationally considered...

.> But ChatGPT and similar programs are, by design, unlimited in what they can “learn” (which is to say, memorize); they are incapable of distinguishing the possible from the impossible. Unlike humans, for example, who are endowed with a universal grammar that limits the languages we can learn to those with a certain kind of almost mathematical elegance, these programs learn humanly possible and humanly impossible languages with equal facility.

.> But ChatGPT and similar programs are, by design, unlimited in what they can “learn” (which is to say, memorize); they are incapable of distinguishing the possible from the impossible. Unlike humans, for example, who are endowed with a universal grammar that limits the languages we can learn to those with a certain kind of almost mathematical elegance, these programs learn humanly possible and humanly impossible languages with equal facility.

.> Whereas humans are limited in the kinds of explanations we can rationally conjecture, machine learning systems can learn both that the earth is flat and that the earth is round. They trade merely in probabilities that change over time.

.> For this reason, the predictions of machine learning systems will always be superficial and dubious.

.> The human mind is not, like ChatGPT and its ilk, a lumbering statistical engine for pattern matching, gorging on hundreds of terabytes of data and extrapolating the most likely conversational response or most probable answer to a scientific question. On the contrary, the human mind is a surprisingly efficient and even elegant system that operates with small amounts of information; it seeks not to infer brute correlations among data points but to create explanations...

.> Indeed, such programs are stuck in a prehuman or nonhuman phase of cognitive evolution. Their deepest flaw is the absence of the most critical capacity of any intelligence: to say not only what is the case, what was the case and what will be the case — that’s description and prediction — but also what is not the case and what could and could not be the case. Those are the ingredients of explanation, the mark of true intelligence.

.> The crux of machine learning is description and prediction; it does not posit any causal mechanisms or physical laws. Of course, any human-style explanation is not necessarily correct; we are fallible. But this is part of what it means to think: To be right, it must be possible to be wrong. Intelligence consists not only of creative conjectures but also of creative criticism. Human-style thought is based on possible explanations and error correction, a process that gradually limits what possibilities can be rationally considered...

.> But ChatGPT and similar programs are, by design, unlimited in what they can “learn” (which is to say, memorize); they are incapable of distinguishing the possible from the impossible. Unlike humans, for example, who are endowed with a universal grammar that limits the languages we can learn to those with a certain kind of almost mathematical elegance, these programs learn humanly possible and humanly impossible languages with equal facility.

.> But ChatGPT and similar programs are, by design, unlimited in what they can “learn” (which is to say, memorize); they are incapable of distinguishing the possible from the impossible. Unlike humans, for example, who are endowed with a universal grammar that limits the languages we can learn to those with a certain kind of almost mathematical elegance, these programs learn humanly possible and humanly impossible languages with equal facility.

.> Whereas humans are limited in the kinds of explanations we can rationally conjecture, machine learning systems can learn both that the earth is flat and that the earth is round. They trade merely in probabilities that change over time.

.> For this reason, the predictions of machine learning systems will always be superficial and dubious.

@rchusid that's the bain of my existence trying to understand #COVID and longer term outcomes, people will confidently report negative results as not having had an episode of COVID. This leaves us with "hospitalized COVID patient" as the exposure variable we can use.
#epidemiology #StatisticalModels

For those interested in employing linear regression using R, this freely available book: https://pyoflife.com/linear-regression-using-r-an-introduction-to-data-modeling/

#datamodeling #R #CodingBook #StatisticalModels #statisticalanalysis #statisticallearning #StatisticalProgramming

For those interested in employing linear regression using R, this freely available book: https://pyoflife.com/linear-regression-using-r-an-introduction-to-data-modeling/

#datamodeling #R #CodingBook #StatisticalModels #statisticalanalysis #statisticallearning #StatisticalProgramming

For those interested in employing linear regression using R, this freely available book: https://pyoflife.com/linear-regression-using-r-an-introduction-to-data-modeling/

#datamodeling #R #CodingBook #StatisticalModels #statisticalanalysis #statisticallearning #StatisticalProgramming

For those interested in employing linear regression using R, this freely available book: https://pyoflife.com/linear-regression-using-r-an-introduction-to-data-modeling/

#datamodeling #R #CodingBook #StatisticalModels #statisticalanalysis #statisticallearning #StatisticalProgramming

After settling here, it is time for a short #introduction.

I'm a #TheoreticalBiologist who turned into #MedicalGenomics and #bioinformatics. Currently, I'm a #postdoc at #ISTA developing #StatisticalModels and #ComputationalTools to analyze large-scale human medical record data. I want to understand how genetics and our lifestyles shape our risk of disease.

Since I am affected by a chronic illness myself, I am particularly interested in: #hEDS #MECFS #SFN #MCAS #Dysautonomia