News and insights in Bayesian probability theory, statistics, and decision theory.
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Bayesian Theory @mander.xyz stravanasu @lemmy.ca (Bayesian) Foundations of data science
A little advertisement for a new free online course about the foundations of data science, machine learning, and – just a little – artificial intelligence. It's been designed for students in computer science and data science, who could be uncomfortable with a head-on probability-theory or statistics approach, and who might have a lighter background in maths. The main point of view of the course is how to build an artificial-intelligence agent who must draw inferences and make decisions. As a course, it's still a sort of experiment.
https://pglpm.github.io/ADA511/
In more technical terms, the course is actually about so-called "Bayesian nonparametric density inference" and Bayesian decision theory.
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Bayesian Theory @mander.xyz fossilesque @mander.xyz 
win-vector.com What You Should Know About Linear Markov ChainsI want to collect some “great things to know about linear Markov chains.” For this note we are working with a Markov chain on states that are the integers 0 through k (k > 0). A Mark…
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Bayesian Theory @mander.xyz fossilesque @mander.xyz 
arxiv.org Bayes in the age of intelligent machinesThe success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference. We argue that this is not the case, and that in fact these systems offer new opportunities for Bayesian ...
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Bayesian Theory @mander.xyz stravanasu @lemmy.sdf.org GPU-accelerated Gibbs sampling
Bayesian nonparametric methods include machine-learning and deep-learning methods as special cases, obtained by making quite coarse approximations to make the computation feasible. Using the fully fledged Bayesian method would give much more, but would take orders of magnitude more computational resources and time.
Papers like this, however, open up many more possibilities to use the full theory rather than approximations. I'm quite surprised to see that this paper is already four years old. I wonder if there's been any progress since then.
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Bayesian Theory @mander.xyz stravanasu @lemmy.ca "Everything that Works Works Because it's Bayesian"
Old post (still true). Almost a meme now :)
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Bayesian Theory @mander.xyz stravanasu @lemmy.ca Three non-probability books with good introductions to Bayesian probability theory
Today there's an abundance of textbooks and webbooks on Bayesian probability theory, decision theory, and statistics, at very diverse technical levels. I wanted to point out three books whose main topic is not probability theory, but which give very good introductions (even superior to those of some specialized textbooks, in my opinion) to Bayesian probability theory:
- Artificial Intelligence: A Modern Approach by S. J. Russell, P. Norvig. Part IV is an amazing introduction to Bayesian theory – including decision theory – with many connections with Artificial Intelligence and Logic.
- Medical Decision Making by H. C. Sox, M. C. Higgins, D. K. Owens. This is essentially a very clear and insightful textbook on Bayesian probability theory and decision theory, but targeted to clinical decision-making.
- [*Sentential Probability Logic: Origins, Development, Current Status, and Technic