LCE Kotisivu

S-114.600 Bayesilaisen mallintamisen perusteet


Lisälukemistoa Bayes-mallintamisesta ja Monte Carlo -menetelmistä

Tämä on aiemmin tekemästäni pidemmästä listasta lyhennetty versio. Useimmat viittaukset ovat kirjoihin, ja lisään vielä muutaman on-line viitteen kun ehdin.

Bayesian data analysis

Sivia, 1996: Data Analysis: A Bayesian Tutorial
Berry, 1996: Statistics: A Bayesian Perspective
Lee, 1997: Bayesian Statistics: An Introduction
Gelman et al., 1995: Bayesian Data Analysis

Bayesian theory

Bernardo and Smith, 1994: Bayesian Theory

Monte Carlo

Gilks et al., editors, 1996: Markov Chain Monte Carlo in Practice
Neal, 1993: Probabilistic Inference using Markov Chain Monte Carlo Methods (Postscript)
MacKay, 1998: Introduction to Monte Carlo Methods in Jordan, editor, 1998: Learning in Graphical Models
MacKay, 2001: Information Theory, Inference and Learning Algorithms, Chapters 27-30. (Postscript/PDF)
Robert and Casella, 1999: Monte Carlo Statistical Methods
Chen, Shao and Ibrahim, 2000: Monte Carlo Methods in Bayesian Computation
Liu, 2001: Monte Carlo Strategies in Scientific Computing
Gamerman, 1997: Markov Chain Monte Carlo: Stochastic simulation for Bayesian inference
Gentle, 1998: Random Number Generation and Monte Carlo Methods
Doucet, de Freitas and Gordon, 2001: Sequential Monte Carlo Methods in Practice (*)
Robert, 1998: Discretization and MCMC Convergence Assessment

Variational (ensemble) methods

MacKay, 2001: Information Theory, Inference and Learning Algorithms, Chapter 31. (Postscript/PDF)
Opper and Saad, 2001 Advanced Mean Field Methods: Theory and Practice
Jaakkola and Jordan, 1999 Variational Methods and the QMR-DT Database in Bishop, editor, 1998: Neural Networks and Machine Learning
Barber and Bishop, 1999 Ensemble Learning in Bayesian Neural Networks in Bishop, editor, 1998: Neural Networks and Machine Learning

Model families

Congdon: Bayesian Statistical Modelling (collection of nearly 200 worked examples with BUGS code available)
Bayesian networks
Jensen, 1996: A introduction to Bayesian Networks
Pearl, 1988: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Generalized Linear Models
Johnson and Albert, 1999: Ordinal Data Modeling
Neural networks
Bishop, 1995: Neural Networks for Pattern Recognition
Neal, 1996: Bayesian Learning for Neural Networks
Gaussian processes
MacKay, 1998: Introduction to Gaussian Processes in Bishop, editor, 1998: Neural Networks and Machine Learning
Neal, 1999: Regression and classification using Gaussian process priors in Bernardo et al., editors, 1999: Bayesian Statistics 6
Time series
West and Harrison, 1997: Bayesian Forecasting and Dynamic Models
Pole et al., 1997: Applied Bayesian Forecasting and Time Series Analysis


Matrix Algebra
Harville, 1997: Matrix Algebra From a Statistician's Perspective
Johnson et al., 1993: Univariate discrete distributions
Johnson et al., 1994: Continuous univariate distributions, volume 1
Johnson et al., 1995: Continuous univariate distributions, volume 2
Johnson et al., 1997: Discrete multivariate distributions
Kotz et al., 2000: Continuous multivariate distributions, volume 1
Jaynes, 1996: Probability theory: The logic of science
Howson and Urbach, 1993: Scientific Reasoning: The Bayesian Approach
Bayes, 1763: An Essay Towards Solving a Problem in the Doctrine of Chances (Reprint available at JSTOR)
Laplace, 1774: Memoir on the Probability of the Causes of Events (Reprint available at JSTOR)
Stigler, 1986: Thomas Bayes's Bayesian Inference (Available at JSTOR)
Stigler, 1975: Napoleonic Statistics: The Work of Laplace (Available at JSTOR)
Stigler, 1986: Laplace's 1774 Memoir on Inverse Probability (Available at JSTOR)
Fienberg, 1992: A Brief History of Statistics in Three and One-Half Chapters: A Review Essay (Available at JSTOR)

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This page has been updated 15.12.2000