#bayesianism — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #bayesianism, aggregated by home.social.
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Jan-Willem Romeijn on päivittänyt SEP-entryään tilastotieteen filosofiasta,
https://plato.stanford.edu/entries/statistics/
#tilastotiede #tiede #tieteenfilosofia #filosofia #statistics #philosophyofScience #epistemology #probability #induction #confirmation #evidence #learning #reasoning #paattely #bayesianism #models
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Vincenzo Crupi has revised his SEP-entry on Confirmation, https://plato.stanford.edu/entries/confirmation/
#confirmation #reasoning #logic #epistemology #philosophy #sep #revised #hypotheses #science #concepts #filosofia #tiede #evidence #induction #probability #bayesianism #statistics
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Igor Douven has revised his SEP-entry on Abduction, https://plato.stanford.edu/entries/abduction/
#abduction #reasoning #logic #epistemology #tieto #filosofia #paattely #philosophy #explanation #induction #peirce #bayesianism #selitys #skepticism
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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!
https://doi.org/10.1017/9781009210171
#philosophy #Bayesianism #induction #confirmation #probability #philosophyofscience
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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!
https://doi.org/10.1017/9781009210171
#philosophy #Bayesianism #induction #confirmation #probability #philosophyofscience
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My long-in-preparation Cambridge Element ‘Probability and Inductive Logic’ is now available.
https://doi.org/10.1017/9781009210171
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.
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https://bigthink.com/13-8/qbism-quantum-physics/
A post promising a series about #quantum #bayesianism. So far so good, this is worth reading to see where it's headed.
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https://bigthink.com/13-8/qbism-quantum-physics/
A post promising a series about #quantum #bayesianism. So far so good, this is worth reading to see where it's headed.
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Beyond the applied ML context, I see a lot of connections to the limitations of #utilitarianism, utilitarian aggregation / #populationEthics, #rationality, #longtermism, subjective #Bayesianism, etc. But would anyone read any of that?
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Beyond the applied ML context, I see a lot of connections to the limitations of #utilitarianism, utilitarian aggregation / #populationEthics, #rationality, #longtermism, subjective #Bayesianism, etc. But would anyone read any of that?
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The No Free lunch theorem implies that we need #Bayesianism
Ok, let me explain. The fact that there cannot be a general purpose algorithm for everything means that we need a prior to solve a certain problem.
Imagine for example that you want to deblur an image, the more a priori you have, the best you will perform. Is it a face or some text? what is the font? what is the language? what is the type or blur (Gaussian kernel, pixelisation, etc)?
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The No Free lunch theorem implies that we need #Bayesianism
Ok, let me explain. The fact that there cannot be a general purpose algorithm for everything means that we need a prior to solve a certain problem.
Imagine for example that you want to deblur an image, the more a priori you have, the best you will perform. Is it a face or some text? what is the font? what is the language? what is the type or blur (Gaussian kernel, pixelisation, etc)?
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A question for epistemologists! When Bayesians talk about degrees of belief, do they have any resources to explain why a 0.5 degree of belief ('no idea whether it's true or false') is a worse epistemic position to be in than a 0.99 or 0.01 degree of belief ('very confident about my judgement')? My sense is that the answer is 'no', and that Bayesian epistemology turns inquiry into mechanical rule-following rather than a goal-directed rational process. #Bayesianism #Philosophy #Epistemology
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A question for epistemologists! When Bayesians talk about degrees of belief, do they have any resources to explain why a 0.5 degree of belief ('no idea whether it's true or false') is a worse epistemic position to be in than a 0.99 or 0.01 degree of belief ('very confident about my judgement')? My sense is that the answer is 'no', and that Bayesian epistemology turns inquiry into mechanical rule-following rather than a goal-directed rational process. #Bayesianism #Philosophy #Epistemology
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CW: What is the favorite sport of mathematicians?
Bayes-Ball! ⚾
:arxiv: https://arxiv.org/abs/1301.7412
Check out this paper on belief networks with the catchiest title I've ever seen
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CW: What is the favorite sport of mathematicians?
Bayes-Ball! ⚾
:arxiv: https://arxiv.org/abs/1301.7412 (Bayes-Ball: The Rational Pastime)
Check out this paper on belief networks with the catchiest title I've ever seen
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🤔 Bayesian Inference (on graphical models) is NP-hard.
But even worst! every epsilon-approximation is also NP-hard.
Which means that the worst case scenario is (almost certainly) exponential.
Good news is, there are some special cases where approximation or exact inference can be performed efficiently.
📘 Check out more in "Probabilistic Graphical Models: Principles and Technique" by Daphne Koller and Nir Friedman
#Bayes #bayesianism #MachineLearning #AI #ML #BayesianInference #Inference
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🤔 Bayesian Inference (on graphical models) is NP-hard.
But even worst! every epsilon-approximation is also NP-hard.
Which means that the worst case scenario is (almost certainly) exponential.
Good news is, there are some special cases where approximation or exact inference can be performed efficiently.
📘 Check out more in "Probabilistic Graphical Models: Principles and Technique" by Daphne Koller and Nir Friedman
#Bayes #bayesianism #MachineLearning #AI #ML #BayesianInference #Inference
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Do you know Judea Pearl?
He bootstrapped the probabilistic approach to AI in his 1988 book
📘 "Probabilistic Reasoning in Intelligent Systems"He also invented Bayesian Networks and contributed greatly to causality.
Most famous for Pearl's Calculus and the do operator.
🏆 Turing award "for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning"
#uncertainty #bayes #bayesianism #AI #ML #MachineLearning #researcher
