This page contains resources about Bayesian Parameter Estimation, Bayesian Parameter Learning and Bayes Estimator.
Subfields and Concepts
- For complete (fully observed data):
- Dirichlet distribution (or other priors)
- For incomplete (hidden/missing data):
- Markov Chain Monte Carlo (MCMC)
- Viterbi Algorithm
- Variational Bayes
- Stochastic Gradient Variational Bayes (SGVB) Estimator
- Bayesian Hierarchical Modelling / Hierarchical Bayes Model
- Hyperparameter
- Hyperprior
- Bayesian Decision Theory
- Bayesian Point Estimation
- Bayesian Signal Processing
- Bayes Risk
- Risk Function / Posterior Expected Loss (i.e. Expectation Value of Loss Function)
- Minimum Mean Square Error (MMSE)
- Maximum Likelihood Estimation (MLE)
- Asymptotics of Maximum Likelihood
- Cramer-Rao bound
- Fisher information
- Conjugate priors
- Uninformative priors / Noninformative priors
- Jeffreys prior
- Exponential family
- Beta distribution
- Bayesian Online Parameter Estimation
- Bayes Filter / Recursive Bayesian Estimation
- Bayesian Density Estimation
- Nonparametric Empirical Bayes (NPEB)
- Parametric Empirical Bayes Point Estimation
- Iterative proportional fitting (IPF)
- Nonparametric Methods
- Kernel Density Estimation (KDE)
- k-Nearest Neighbours
- Bayesian Nonparametrics
Books and Book Chapters
- Theodoridis, S. (2015). "Chapter 12: Bayesian Learning" Machine Learning: A Bayesian and Optimization Perspective. Academic Press.
- Aster, R. C., Borchers, B., & Thurber, C. (2012). "Chapter 11: Bayesian Methods". Parameter estimation and inverse problems. Academic Press
- Barber, D. (2012). "Section 9.1: Learning as Inference". Bayesian Reasoning and Machine Learning. Cambridge University Press.
- Barber, D. (2012). "Chapter 18: Bayesian Linear Models". Bayesian Reasoning and Machine Learning. Cambridge University Press.
- Duda, R. O., Hart, P. E., & Stork, D. G. (2012). "Chapter 2: Bayesian Decision Theory". Pattern Classification. John Wiley & Sons.
- Murphy, K. P. (2012). "Section 5.7: Bayesian Decision Theory". Machine Learning: A Probabilistic Perspective. MIT Press.
- Koller, D., & Friedman, N. (2009). "Chapter 17: Parameter Estimation". Probabilistic Graphical Models. MIT Press.
- Theodoridis, S., Pikrakis, A., Koutroumbas, K., & Cavouras, D. (2008). "Chapter 2: Bayesian Decision Theory". Pattern Recognition. 4th Ed. Academic Press.
- Bishop, C. M. (2006). "Chapter 2: Probability Distributions". Pattern Recognition and Machine Learning. Springer.
- MacKay, D. J. (2003). "Chapter 24: Exact Marginalization". Information Theory, Inference and Learning Algorithms. Cambridge University Press.
- MacKay, D. J. (2003). "Chapter 36: Decision Theory". Information Theory, Inference and Learning Algorithms. Cambridge University Press.
- Bretthorst, G. L. (1998). Bayesian spectrum analysis and parameter estimation (Vol. 48). Springer Science & Business Media.
- Berger, J. O. (1993). Statistical decision theory and Bayesian analysis. 2nd Ed. Springer Science & Business Media.
Scholarly Articles
- Ghahramani, Z. (2003). "Graphical models: Parameter learning". In Handbook of Brain Theory and Neural Networks.
See also
Other resources
- Bayesian parameter estimation - Metacademy