Ipynotebook machinelearningThis contains a number of IP[y]: Notebooks that hopefully give a light to areas of bayesian machine learning.
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PyroDeep universal probabilistic programming with Python and PyTorch
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ResourcesPyMC3 educational resources
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NumpyroProbabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
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LooperA resource list for causality in statistics, data science and physics
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Scikit StanA high-level Bayesian analysis API written in Python
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PycuriousPython package for computing the Curie depth from the magnetic anomaly
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Rhat essRank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC
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Bayesian Neural NetworksPytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
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Ggdistributeggplot2 extension for plotting distributions
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Brmsbrms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
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Bda py demosBayesian Data Analysis demos for Python
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Pytorch BayesiancnnBayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
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NotesResources to learn more about Machine Learning and Artificial Intelligence
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RstanRStan, the R interface to Stan
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Pymc3Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara
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Dsge.jlSolve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
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Dbda PythonDoing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python/PyMC3 code
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Bda r demosBayesian Data Analysis demos for R
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Vae cfVariational autoencoders for collaborative filtering
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BayaderaHigh-performance Bayesian Data Analysis on the GPU in Clojure
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PyvarinfPython package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch
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Rstanarmrstanarm R package for Bayesian applied regression modeling
Stars: ✭ 285 (+630.77%)