| Monte Carlo Methods
|
Markov chain Monte Carlo (MCMC) methods use sampling to approximate
high dimensional integrals and intractable sums. MCMC methods are
widely used in many areas of science, applied mathematics and
engineering. They are an indispensable approximate inference tool for
Bayesian statistics and machine learning.
Some relevant publications:
- Murray, I.A., Ghahramani, Z., and MacKay, D.J.C. (2006)
MCMC for doubly-intractable
distributions.
In Uncertainty in Artificial Intelligence (UAI-2006).
- Murray, I., MacKay, D.J.C., Ghahramani, Z. and Skilling,
J. (2006)
Nested Sampling for
Potts Models.
In Advances in Neural Information Processing
Systems 18 (NIPS-2005).
- Murray, I. and Ghahramani, Z. (2004)
Bayesian Learning in
Undirected Graphical Models: Approximate MCMC
algorithms.
In
Uncertainty in Artificial Intelligence
(UAI-2004).
-
Rasmussen, C. E. and Ghahramani, Z. (2003)
Bayesian Monte
Carlo.
In Advances in Neural Information Processing
Systems 15. Cambridge, MA: MIT Press.