The bias-variance dilemma of the Monte Carlo method

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2130, Issue , 2001, Pages 141-147

  Mark, Zlochin ^a   Baram, Yoram ^a  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; NEURAL NETWORKS;

BIASED APPROXIMATION; FORMAL SETTING; LOWER APPROXIMATION; MONTE-CARLO APPROXIMATIONS; STATISTICAL INFERENCE; UNBIASED ALGORITHMS;

MONTE CARLO METHODS;

EID: 84958960772     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44668-0_20     Document Type: Conference Paper

References (6)

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2. W.R. Gilks, S. Richardson, and D.J. Spiegelhalter, editors. Markov Chain Monte Carlo in Practice.Chapman&Hall, 1996.
3. 3.W. Hardle. Applied nonparametric regression.Cambridge University Press, 1990.
4. A.M. Horowitz. A generalized guided Monte Carlo algorithm. Physics Letters B, 268:247–252, 1991.
5. R.M. Neal. Bayesian Learning for Neural Networks.Springer-Verlag, 1996.
6. R.M. Neal. Annealed importance sampling. Technical Report No.9805, Dept.of Statistics, University of Toronto, 1998.