S. C. Heath
- Boehnke, M., Lange, K., & Cox, D.R., 1991. Statistical methods for multipoint radiation hybrid mapping. Am. J. Hum. Genet. 49, 1174-1188.Google Scholar
- Cox, D.R., Burmeister, M., Price, E.R., Kim, S., & Myers, R.M., 1990. Radiation hybrid mapping: A somatic cell genetic method for constructing high-resolution maps of mammalian chromosomes. Science 250, 245-250.Google ScholarCross Ref
- Geyer, C.J., 1992. Practical Markov chain Monte Carlo (with discussion). Statist. Sci. 473-511.Google ScholarCross Ref
- Geyer, C.J., & Thompson, E.A., 1995. Annealing Markov chain Monte Carlo with applications to ancestral inference. J. Am. Star. Assoc. 90, 909-920.Google ScholarCross Ref
- Goss, S.J., & Harris, H., 1975. New method for mapping genes in human chromosomes. Nature 255, 680-864.Google ScholarCross Ref
- Hastings, W.K., 1970. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 97-109.Google ScholarCross Ref
- Lange, K., & Boehnke, M., 1992. Bayesian methods and optimal experimental design for gene mapping by radiation hybrids. Ann. Hum. Genet. 56, 118-144.Google ScholarCross Ref
- Lawrence, S., Morton, N., /~ Cox, D., 1991. Radiation hybrid mapping. Proc. Natn. Acad. Sci. U.S.A. 88, 7477- 7480.Google ScholarCross Ref
- Metropolis, N., Rosenbluth, A., Teller, M., & Teller, E., 1953. Equations of state calculations by fast computing machines. J. Chem. Phys. 21, 1087-1091.Google ScholarCross Ref
- Myldand, P., Tierney, L., 2~ Yu, B., 1995. Regeneration in Markov chain samplers. J. Am. Stat. Assoc. 90, 233- 241.Google ScholarCross Ref
- Ripley, B.D., 1978. Stochastic Simulation. John Wiley, New York. Google ScholarDigital Library
- Walter, M.A., Spillett, D.J., Thomas, P., Weissenbach, J., & Goodfellow, P.N., 1995. A method for constructing radiation hybrid maps of whole genomes. Nature Genet. 7, 22-28.Google ScholarCross Ref
Index Terms
- The application of Markov chain Monte Carlo methods to radiation hybrid mapping
Recommendations
Pseudo-marginal Markov Chain Monte Carlo for Nonnegative Matrix Factorization
A pseudo-marginal Markov chain Monte Carlo (PMCMC) method is proposed for nonnegative matrix factorization (NMF). The sampler jointly simulates the joint posterior distribution for the nonnegative matrices and the matrix dimensions which indicate the ...
Analyzing Markov chain Monte Carlo output
AbstractMarkov chain Monte Carlo (MCMC) is a sampling‐based method for estimating features of probability distributions. MCMC methods produce a serially correlated, yet representative, sample from the desired distribution. As such it can be difficult to ...
Visual representation of the multivariate correlation structure in a Markov chain. Output analysis that accounts for this multivariate structure is able to more accurately summarize variability in the simulation process. We review the output analysis ...
Interacting particle Markov chain Monte Carlo
ICML'16: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like related methods, iPMCMC is a Markov chain Monte Carlo sampler on an ...
Comments