PyRational

I have implemented a Python library for modelling, inference and updating with Almost Desirable Gambles (ADG) models. It is both friendly and flexible. It works with continuous, discrete and mixed variables. Here you can find some additional info, setup instructions and 4 examples (notebooks): https://github.com/PyRational/PyRational/blob/master/notebooks/index.ipynb The notebooks (and relative examples) …

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Baycomp

Janez Demsar has reimplemented our library about Bayesian hypothesis testing for comparing competing algorithms in ML. It can now be installed directly with pip. Hereafter, a brief description. Baycomp is a library for Bayesian comparison of classifiers. Functions compare two classifiers on one or on multiple data sets. They compute …

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Bayes+Hilbert=QM

QM is based on four main axioms, which were derived after a long process of trial and error. The motivations for the axioms are not always clear and even to experts the basic axioms of QM often appear counter-intuitive. In a recent paper [1], we have shown that: It is …

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Bayesian Signed-Rank Test for comparing algorithms in machine learning

This post is about Bayesian nonparametric tests for comparing algorithms in ML. This time we will discuss about Python module signrank in bayesiantests (see our GitHub repository). It computes the Bayesian equivalent of the Wilcoxon signed-rank test. It return probabilities that, based on the measured performance, one model is better …

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28 October, USA general election situation

These are the updated probability obtained running the Python code that computes the Bayesian posterior distribution over the electoral votes using near-ignorance priors. The worst and best case distribution for Clinton are in red and respectively, blue.. Winning probability above 0.99 (for both worst and best scenario). Electoral votes between …

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