This page contains resources about Bayesian Inference and Bayesian Machine Learning.
Subfields and ConceptsEdit
- Bayesian Model Selection / Bayesian Model Comparison
- Bayes Factor
- Bayesian Model Averaging (in Ensemble Learning)
- Bayesian Information Criterion (BIC)
- Bayesian Parameter Estimation
- Bayesian Feature Selection / Bayesian Variable Selection
- Spike and Slab Method
- Kuo & Mallick
- Gibbs Variable Selection (GVS)
- Stochastic Search Variable Selection (SSVS)
- Adaptive shrinkage with Jeffreys' prior or Laplace prior
- Reversible jump MCMC
- Variational Bayesian Inference
- Approximate Bayesian Computation (ABC)
- Automatic Variational ABC
- Variational Bayes with Intractable Likelihood (VBIL)
- Bayesian Information Theory
- The Principle of Maximum Entropy
- Bayesian Occam's Razor
- Minimum Message Length (MML)
- Bayesian Linear (Regression) Model
- Bayesian Naive Bayes
- Bayesian Mixture Models
- Sparse Bayesian Models
- Bayesian LASSO
- Bayesian Ridge Regression
- Relevance Vector Machine (RVM) / Automatic Relevance Determination / Sparse Bayesian Learning
- The Spike and Slab Model
- Bayesian State Space Models
- Bayesian Linear Dynamical System
- Bayesian Time Series
- Bayesian Structural Time Series (BSTS)
- Kalman filter
- Spike and Slab Method
- Bayesian Model Averaging
- Probabilistic Matrix Factorization
- Bayesian Multitask Learning
- Bayesian Optimization
- Bayesian Reinforcement Learning
- Bayesian Neural Network
- Bayesian Deep Learning
- Bayesian Learning by Zoubin Ghahramani - VideoLectures.Net
- Graphical modelling and Bayesian structural learning by Peter Green - VideoLectures.Net
- CSC 2541: Topics in Machine Learning: Bayesian Methods for Machine Learning by Radford Neal
- CSE 515T: Bayesian Methods in Machine Learning by Roman Garnett
- Advanced Statistical Machine Learning by Stefanos Zafeiriou
Books and Book ChaptersEdit
- Davidson-Pilon, C. (2015). Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference. Addison-Wesley Professional.
- Koduvely, H. M. (2015). Learning Bayesian Models with R. Packt Publishing.
- Theodoridis, S. (2015). "Section 12: Bayesian Learning". Machine Learning: A Bayesian and Optimization Perspective. Academic Press.
- Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2014). Bayesian data analysis. 3rd Ed. Chapman & Hall/CRC.
- Lee, P. M. (2012). Bayesian statistics: an introduction. 4th Ed. John Wiley & Sons.
- Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press.
- Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern Classification. John Wiley & Sons.
- Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
- Berger, J. O. (2010). Statistical decision theory and Bayesian analysis. 2nd Ed. Springer Science & Business Media.
- Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models. MIT Press.
- Barber, D., Cemgil, A. T., & Chiappa, S. (2011). Bayesian time series models. Cambridge University Press.
- Bishop, C. M. (2006). "Section 2.2: The beta distribution". Pattern Recognition and Machine Learning. Springer.
- MacKay, D. J. (2003). "Chapter 37: Bayesian Inference and Sampling Theory". Information Theory, Inference and Learning Algorithms. Cambridge University Press.
- Mitchell, T. M. (1997). "Chapter 6: Bayesian Learning". Machine Learning. McGraw Hill.
- Pearl, J. (1988). "Chapter 2: Bayesian Inference". Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann.
See also Further Reading.
- Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., & de Freitas, N. (2016). Taking the human out of the loop: A review of bayesian optimization. Proceedings of the IEEE, 104(1), 148-175.
- Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452-459.
- Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015). Weight Uncertainty in Neural Network. In Proceedings of The 32nd International Conference on Machine Learning (pp. 1613-1622).
- Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., & Adams, R. P. (2015, February). Scalable bayesian optimization using deep neural networks. In International Conference on Machine Learning.
- Heckerman's Bayes Net Learning Tutorial
- A Brief Introduction to Graphical Models and Bayesian Networks
- A brief introduction to Bayes' Rule
- An Introduction to Graphical Models by M. Jordan
- Bayesian Modelling in Machine Learning: A Tutorial Review
- Bayesian Methods for Machine Learning - NIPS 2004
- Bayesian Machine Learning by Ian Murray
- Bayesian Machine Learning by Zoubin Ghahramani
- Dynamical Systems, Stochastic Processes and Bayesian Inference - NIPS 2016 workshop
- Bayesian Probabilistic Matrix Factorization - MATLAB
- Bayesian Modeling and Monte Carlo Methods - MATLAB
- Bayesian Optimization (Statistics and Machine Learning Toolbox) - MATLAB
- Bayesian Methods for Hackers - Python
- Infer.NET - Developed by Microsoft Research
- OpenBUGS - Bayesian Inference Using Gibbs Sampling
- Probability Theory
- Information Theory
- Monte Carlo Methods
- Probabilistic Graphical Models
- Stochastic Processes and Random Fields
- Linear Dynamical Systems
- Bayesian Inference with PyMC3 (Part 1, Part 2, Part 3) - Python
- A Bayesian Approach to Monitoring Process Change (Part 1, Part 2, Part 3) - Python
- Bayesian Inference in R
- Bayesian machine learning - Introduction
- Bayesian machine learning - FastML
- Bayesian machine learning - Metacademy
- Bayesian Statistics - Scholarpedia
- Are "Bayesian networks" Bayesian? - No, Bayesian and Frequentist approaches can both be used.
- Bayesian Deep Learning - NIPS 2016 workshop
- Bayesian Methods Research Group
- Bayesian Learning for Statistical Classification - blog post
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