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MCSim User' Manual

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Bibliographic References

Barry, T. M. (1996). Recommendations on the testing and use of pseudo-random number generators used in Monte Carlo analysis for risk assessment. Risk Analysis 16:93-105.

Bernardo, J. M. and Smith, A. F. M. (1994). Bayesian Theory. Wiley, New York.

Bois, F. Y., Gelman, A., Jiang, J., Maszle, D., Zeise, L. and Alexeef, G. (1996). Population toxicokinetics of tetrachloroethylene. Archives of Toxicology 70:347-355.

Bois, F. Y., Zeise, L. and Tozer, T. N. (1990). Precision and sensitivity analysis of pharmacokinetic models for cancer risk assessment: tetrachloroethylene in mice, rats and humans. Toxicology and Applied Pharmacology 102:300-315.

Gear, C. W. (1971a). Algorithm 407 - DIFSUB for solution of ordinary differential equations [D2]. Communications of the ACM 14:185-190.

Gear, C. W. (1971b). The automatic integration of ordinary differential equations. Communications of the ACM 14:176-179.

Gelman, A. (1992). Iterative and non-iterative simulation algorithms. Computing Science and Statistics 24:433-438.

Gelman, A., Bois, F. Y. and Jiang, J. (1996). Physiological pharmacokinetic analysis using population modeling and informative prior distributions. Journal of the American Statistical Association 91:1400-1412.

Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (1995). Bayesian Data Analysis. Chapman & Hall, London.

Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences (with discussion). Statistical Science 7:457-511.

Hammersley, J. M. and Handscomb, D. C. (1964). Monte Carlo Methods. Chapman and Hall, London.

Manteufel, R. D. (1996). Variance-based importance analysis applied to a complex probabilistic performance assessment. Risk Analysis 16:587-598.

Park, S. K. and Miller, K. W. (1988). Random number generators: good ones are hard to find. Communications of the ACM 31:1192-1201.

Press, W. H., Flannery, B. P., Teukolsky, S. A. and Vetterling, W. T. (1989). Numerical Recipes (2st ed.). Cambridge University Press, Cambridge.

Smith, A. F. M. (1991). Bayesian computational methods. Philosophical Transactions of the Royal Society of London, Series A 337:369-386.

Smith, A. F. M. and Roberts, G. O. (1993). Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society Series B 55:3-23.

Vattulainen, I., Ala-Nissila, T. and Kankaala, K. (1994). Physical tests for random numbers in simulations. Physical Review Letters 73:2513-2516.

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