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awesome-bayes
List of resources for bayesian inference
https://github.com/dimenwarper/awesome-bayes
Last synced: 3 days ago
JSON representation
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Resources, papers, and blogs
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Introductory
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- Count Bayesie
- How to become a Bayesian in eight easy steps
- Introduction to Bayesian Statistics
- Michael Jordan's Bayesian Statistics Course Notes
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
- How to become a Bayesian in eight easy steps
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General topics
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MCMC
- The MCMC handbook intro to MCMC - frills intro to MCMC
- Scaling up Bayesian inference
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Variational Inference
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Empirical Bayes
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Non-parametrics
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INLA
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Bayesian deep learning
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Misc
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Michael Betancourt's case studies
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Books
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Software/packages
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General inference
- BUGS
- JAGS - focused on cross-platform, usability. Also tried and tested. R and python bindings too.
- Stan - featured Bayesian inference with R and python bindings. Based on Hamiltonian MC and NUTS. Current favorite of the community it seems with lots of examples, docs.
- edward2/tfprobability
- Zhusuan
- Pyro
- LaplacesDemon
- WebPPL
- Turing.jl
- Infer.NET
- Brancher
- Brancher
- Brancher
- Brancher
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Specific
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Misc
- List of Bayesian inference packages for R
- StatSim - based interface to create, share, and perform inference on probabilistic models. Powered by WebPPL and PyMC3.
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People
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Michael Betancourt's case studies
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Sub Categories
Keywords
bayesian-inference
3
machine-learning
2
artificial-intelligence
1
bayesian-neural-networks
1
bayesian-statistics
1
hamiltonian-monte-carlo
1
hmc
1
julia-language
1
mcmc
1
probabilistic-graphical-models
1
probabilistic-inference
1
probabilistic-models
1
probabilistic-programming
1
turing
1
brms
1
multilevel-models
1
r-package
1
stan
1
statistical-models
1