[1] Abramowitz, M., and Stegun, I.A. 1965. Handbook of Mathematical Functions. Washington DC: National Bureau of Standards.
[2] Akaike, H. 1974. A new look at statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.
[3] Alimov, V. Kh., Mayer, M., and Roth, J. 2005. Differential cross-section of the D(3He, p)4He nuclear reaction and depth profiling of deuterium up to large depths. Nuclear Instruments and Methods in Physics B, 234, 169–175.
[4] Ambegaokar, V., and Troyer, M. 2010. Estimating errors reliably in Monte Carlo simulations of the Ehrenfest model. American Journal of Physics, 78(2), 150.
[5] Amsel, G., and Lanford, W. A. 1984. Nuclear reaction techniques in materials analysis. Annual Review Nuclear and Particle Science, 34, 435–460.
[6] Anton, M., Weisen, H., Dutch, M. J., and von der Linden, W. 1996. X-ray tomography on the TCV tokamak. Plasma Physics and Controlled Fusion, 38, 1849–1878.
[7] Atkinson, A. C., Donev, A., and Tobias, R. 2007. Optimum Experimental Designs, with SAS. Oxford: Oxford University Press.
[8] Bailey, N.T. J. 1990. The Elements of Stochastic Processes. New York: John Wiley & Sons.
[9] Ballentine, L. E. 2003. Quantum Mechanics: A Modern Development. Singapore: World Scientific Publishing.
[10] Bayes, Rev. T. 1763. Essay toward solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society London, 53, 370–418.
[11] Bellman, R. E. 1957. Dynamic Programming. Princeton, NJ: Princeton University Press.
[12] Berger, J. O. 1985. Statistical Decision Theory and Bayesian Analysis. New York: Springer.
[13] Berger, J. O., and Wolpert, R. L. 1988. The Likelihood Principle, 2nd edn. Hayward, CA: IMS.
[14] Bernardo, J. M. 1979. Expected information as expected utility. Annals of Statistics, 7(3), 686–690.
[15] Bernardo, J.M., and Smith, A.F.M. 2000. Bayesian Theory. Chichester, UK: John Wiley & Sons.
[16] Binder, K. 1986. Monte Carlo Methods in Statistical Physics, 2nd edn. Topics in Current Physics, vol. 1. Berlin: Springer-Verlag.
[17] Borth, D. M. 1975. A total entropy criterion for the dual problem of model discrimination and parameter estimation. Journal of the Royal Statistical Society, Series B, 37(1), 77–87.
[18] Bremaud, P. 1999. Markov Chains, Gibbs Fields, Monte Carlo Simulation, and Queues. New York: Springer.
[19] Bretthorst, G. L. 1988. Bayesian Spectrum Analysis and Parameter Estimation. New York: Springer.
[20] Bretthorst, G. L. 1990. Bayesian analysis I. Parameter estimation using quadrature NMR models. Journal of Magnetic Resonance, 88, 533–551.
[21] Bretthorst, G. L. 2001. Generalizing the Lomb-Scargle periodogram. In Mohammad-Djafari, A. (ed.), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, vol. 568. Melville, NY: AIP; pp 241–245.
[22] Bretthorst, G. L. (ed.). 1988. Bayesian Spectrum Analysis and Parameter Estimation. Berlin: Springer-Verlag.
[23] Bretthorst, G. L. 1988. Excerpts from Bayesian spectrum analysis and parameter estimation. In Erickson, G. J., and Smith, C. R. (eds), Maximum Entropy and Bayesian Methods in Science and Engineering, vol. 1. Dordrecht: Kluwer Academic Publishers; p. 75.
[24] Bretthorst, G. L. 1993. On the difference in means. In Grandy, W.T. and Milonni, P.W. (eds), Physics and Probability Essays in Honor of Edwin T. Jaynes, vol. 1. Cambridge: Cambridge University Press; pp 177–194.
[25] Brockwell, A. E., and Kadane, J. B. 2003. A gridding method for Bayesian sequential decision problems. Journal of Computational and Graphical Statistics, 12(3), 566–584.
[26] Bryson, B. 2010. At Home: A Short History of Private Life. London: Transworld Publishers.
[27] Buck, B., and Macaulay, V. A. (eds). 1991. Maximum Entropy in Action. Oxford: Clarendon Press.
[28] Chaloner, K., and Verdinelli, I. 1995. Bayesian experimental design: A review. Statistical Science, 10, 273–304.
[29] Chernoff, H. 1972. Sequential Analysis and Optimal Design. Philadelphia, PA: SIAM.
[30] Chick, S. E., and Ng, S. H. 2002. Joint criterion for factor identification and parameter estimation. In Yücesan, E., Chen, C.-H., Snowdon, J.L., and Charnes, J. M. (eds), Proceedings of the 2002 Winter Simulation Conference; pp 400–406.
[31] Chopin, N., and Robert, C.P. 2010. Properties of nested sampling. Biometrika, 97(3), 741–755.
[32] Clyde, M., Müller, P., and Parmigiani, G. 1993. Optimal design for heart defibrilla-tors. In Case Studies in Bayesian Statistics, II. Berlin: Springer-Verlag; pp 278–292.
[33] Cohen, E. R., and Taylor, B. N. 1987. The 1986 adjustment of the fundamental physical constants. Reviews of Modern Physics, 59(4), 1121–1148.
[34] Cornu, A., and Massot, R. 1979. Compilation of Mass Spectral Data. London: Heyden.
[35] Cox, R. T. 1946. Probability, frequency and reasonable expectation. American Journal of Physics, 14, 1–13.
[36] Cox, R. T. 1961. The Algebra of Probable Inference. Baltimore, MD: Johns Hopkins Press.
[37] Currin, C., Mitchell, T., Moris, M., and Ylvisaker, D. 1991. Bayesian predictions of deterministic functions, with applications to the design and analysis of computer experiments. Journal of the American Statistical Association, 86(416), 953–963.
[38] Daghofer, M., and von der Linden, W. 2004. Perfect Tempering. AIP Conference Proceedings, vol. 735, p. 355.
[39] DasGupta, A. 1996. Review of optimal Bayes design. In Gosh, S., and Rao, C. R. (eds), Handbook of Statistics 13: Design and Analysis of Experiments. Amsterdam: Elsevier.
[40] DeGroot, M. H. 1962. Uncertainty, information and sequential experiments. Annals of Mathematical Statistics, 33(2), 404–419.
[41] DeGroot, M. H. 1986. Concepts of information based utility. In Daboni, L., Montesano, A., and Lines, M. (eds), Recent Developments in the Foundations of Utility and Risk Theory. Dordrecht: Reidel; pp 265–275.
[42] Devroye, L. 1986. Non-uniform Random Variate Generation. New York: Springer.
[43] Dittrich, W., and Reuter, M. 2001. Classical and Quantum Dynamics. New York: Springer.
[44] Dlugosz, S., and Müller-Funk, U. 2009. The value of the last digit: Statistical fraud detection with digit analysis. In Advances in Data Analysis and Classification, vol. 3. New York: Springer.
[45] Dobb, J. L. 1953. Stochastic Processes. New York: John Wiley & Sons.
[46] Doetsch, G. (ed.). 1976. Einführung und Anwendung der Laplace-Transformation. Basel: Birkhauser-Verlag.
[47] Dose, V. 2007. Bayesian estimate of the Newtonian constant of gravitation. Measurement Science and Technology, 18, 176–182.
[48] Dose, V. 2009. Analysis of rare-event time series with application to Caribbean hurricane data. Europhysics Letters, 85, 59001.
[49] Dose, V., and Menzel, A. 2004. Bayesian analysis of climate change impacts in phenology. Global Change Biology, 10, 259–272.
[50] Dose, V., Fauster, Th., and Gossmann, H.J. 1981. The inversion of autoconvolution integrals. Journal of Computational Physics, 41(1), 34–50.
[51] Dose, V., von der Linden, W., and Garrett, A. 1996. A Bayesian approach to the global confinement time scaling in W7AS. Nuclear Fusion, 36, 735–744.
[52] Dose, V., Preuss, R., and Roth, J. 2001. Evaluation of chemical erosion data for carbon materials at high ion fluxes using Bayesian probability theory. Journal of Nuclear Materials, 288, 153–162.
[53] Dose, V., Neuhauser, J., Kurzan, B., Murmann, H., Salzmann, H., and ASDEX Upgrade Team. 2001. Tokamak edge profile analysis employing Bayesian statistics. Nuclear Fusion, 41(11), 1671–1685.
[54] Dreier, H., Dinklage, A., Fischer, R., Hirsch, M., Kornejew, P., and Pasch, E. 2006. Bayesian design of diagnostics: Case studies for Wendelstein 7-X. Fusion Science and Technology, 50, 262–267.
[55] Dreier, H., Dinklage, A., Fischer, R., Hirsch, M., and Kornejew, P. 2006. Bayesian design of plasma diagnostics. Review of Scientific Instruments, 77, 10F323.
[56] Dreier, H., Dinklage, A., Fischer, R., Hirsch, M., and Kornejew, P. 2008. Bayesian experimental design of a multi-channel interferometer for Wendelstein 7-X. Review of Scientific Instruments, 79, 10E712.
[57] Dreier, H., Dinklage, A., Fischer, R., Hirsch, M., and Kornejew, P. 2008. Comparative studies to the design of the interferometer at W7-X with respect to technical boundary conditions. In Hartfuss, H. J., Dudeck, M., Musielok, J., and Sadowski, M.J. (eds), PLASMA 2007, vol. 993. Melville, NY: AIP; pp 183–186.
[58] Dueck, G. 1993. New optimization heuristics: The Great Deluge algorithm and the record-to-record travel. Journal of Computational Physics, 104(1), 86–92.
[59] Economou, E.N. 1979. Green's Functions in Quantum Physics. Springer Series in Solid State Physics, vol. 1. Berlin: Springer-Verlag.
[60] Edwards, W., Lindman, H., and Savage, L. J. 1963. Bayesian statistical inference for psychological research. Psychological Review, 70, 193–242.
[61] Elstrodt, J. 1999. Maß- und Integrationstheorie (Das Lebesgue-Integral, S. 118160.). 2. edn. Heidelberg: Springer.
[62] Emsh, G.G., Eggerfeldt, G.C., and Streit, L. (eds). 1994. On Klauder's Path: A Field Trip. Singapore: World Scientific.
[63] Enzensberger, H. M. 2011. Fatal Numbers: Why Count on Chance. New York: Upper Westside Philosophers.
[64] Ertl, K., von der Linden, W., Dose, V., and Weller, A. 1996. Maximum entropy based reconstruction of soft X-ray emissivity profiles in W7-AS. Nuclear Fusion, 36, 1477–1488.
[65] Evans, M. 2007. Discussion of nested sampling for Bayesian computations by John Skilling. In Bernardo, J. M., Bayarri, M. J., Berger, J. O., Dawid, A. P., Heckermann, D., Smith, A. F. M., and West, M. (eds), Bayesian Statistics, vol. 8. Oxford: Oxford University Press; pp 491–524.
[66] Fedorov, V. V. 1972. Theory of Optimal Experiments. New York: Academic Press.
[67] Feller, W. 1968. An Introduction to Probability Theory and Applications I and II. New York: John Wiley & Sons.
[68] Fischer, R. 2004. Bayesian experimental design - Studies for fusion diagnostics. In Fischer, R., Preuss, R., and von Toussaint, U. (eds), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, vol. 735. Melville, NY: AIP; pp 76–83.
[69] Fischer, R., von der Linden, W., and Dose, V. 1996. On the importance of a marginalization in maximum entropy. In Silver, R. N., and Hanson, K. M. (eds), Maximum Entropy and Bayesian Methods. Dordrecht: Kluwer Academic Publishers; p. 229.
[70] Fischer, R., Hanson, K. M., Dose, V., and von der Linden, W. 2000. Background estimation in experimental spectra. Physical Review E, 61, 1152.
[71] Fischer, R., Dreier, H., Dinklage, A., Kurzan, B., and Pasch, E. 2005. Integrated Bayesian experimental design. In Knuth, K. H., Abbas, A. E., Morris, R. D., and Castle, J. P. (eds), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, vol. 803. Melville, NY: AIP; pp 440–447.
[72] Fisher, R. A. 1925. Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.
[73] Fisher, R.A. 1990. Statistical Methods and Scientific Inference. Oxford: Oxford University Press.
[74] Fishman, G. S. 1996. Monte Carlo: Concepts, algorithms and applications. Berlin: Springer-Verlag.
[75] Freedman, D., Pisani, R., and Purves, R. 2007. Statistics, 4th edn. New York: W. W. Norton & Co.
[76] Frieden, B. R. 1991. Probability, Statistical Optics, and Data Testing. Springer Series in Information Sciences, no. 10. Berlin: Springer-Verlag.
[77] Frieden, R., and Gatenby, R.A. 2007. Exploratory Data Analysis Using Fisher Information. New York: Springer.
[78] Frohner, F. H. 2000. Evaluation and Analysis of Nuclear Resonance Data, JEFF Report 18. Paris: OECD Nuclear Energy Agency.
[79] Fukuda, Y. 2010. Appearance potential spectroscopy (APS): Old method, but applicable to study nano-structures. Analytical Sciences, 26, 187–197.
[80] Garrett, A. J.M. 1991. Ockham's razor. Physics World, May, 39.
[81] Gauss, C. F. 1873. Gottingische gelehrte Anzeigen 1823. In C. F. Gauss, Werke IV. Gottingen: Königliche Gesellschaft der Wissenschaften zu Gottingen; pp 100–104.
[82] Gautier, R., and Pronzato, L. 1998. Sequential design and active control. In New Developments and Applications in Experimental Design, vol. 34. Hayward, CA: Institute of Mathematical Statistics; pp 138–151.
[83] Geyer, C. L., and Williamson, P. P. 2004. Detecting fraud in data sets using Benford's law. Communications in Statistics - Simulation and Computation, 33(1), 229–246.
[84] Gilks, W.R., Richardson, S., and Spiegelhalter, D.J. 1997. Markov Chain Monte CarloinPractice. London: Chapman Hall.
[85] Goggans, P. M., and Chi, Y. 2007. Electromagnetic induction landmine detection using Bayesian model comparison. In Djafari, A. M. (ed.), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, vol. 872. Melville, NY: AIP; pp 533–540.
[86] Goldstein, M. 1992. Comment on ‘Advances in Bayesian experimental design by I. Verdinelli’. In Bernardo, J. M., Berger, J. O., Dawid, A. P., and Smith, A. F. M. (eds), Bayesian Statistics, vol. 4. Oxford: Oxford University Press; p. 477.
[87] Goodman, S.N. 1999. Toward evidence-based medical statistics. 1: The P value fallacy. Annals of Internal Medicine, 130(12), 995–1004.
[88] Gralle, A. 1924. Über die geodätischen Arbeiten von Gauss. In C. F. Gauss, Werke XI Abt. 2. Gottingen: Gesellschaft der Wissenschaften zu Gottingen; p. 14.
[89] Grandy, W. T. 1987. Foundations of Statistical Mechanics I, II. Amsterdam: Kluwer Academic Publishers.
[90] Gregory, P. 2005. Bayesian Logical Data Analysis for the Physical Sciences. Cambridge: Cambridge University Press.
[91] Grinstead, C. M., and Snell, J. L. 1998. Introduction to Probability. Providence, RI: American Mathematical Society.
[92] Gull, S.F. 1989. Bayesian data analysis: straight-line fitting. In Skilling, J. (ed.), Maximum Entropy and Bayesian Methods (1988). Dordrecht: Kluwer Academic Publishers; p. 511.
[93] Gull, S. F. 1989. Developments in maximum entropy data analysis. In Skilling, J. (ed.), Maximum Entropy and Bayesian Methods. Dordrecht: Kluwer Academic Publishers; p. 53.
[94] Gull, S. F., and Daniell, G. J. 1978. Image reconstruction from incomplete and noisy data. Nature, 272, 686.
[95] Gull, S. F., and Skilling, J. 1984. Maximum entropy method in image processing. IEEE Proceedings, 131 F, 646.
[96] Hammersley, H. 1965. Monte Carlo Methods. London: Methiens Monographs.
[97] Harney, H. L. 2003. Bayesian Inference: Parameter Estimation and Decisions. Berlin: Springer-Verlag.
[98] Hjalmarsson, H. 2005. From experiment design to closed-loop control. Automatica, 41, 393–438.
[99] Horiguchi, T., and Morita, T. 1975. Note on the lattice Green's function for the simple cubic lattice. Journal of Physics C, 8, L232.
[100] Hukushima, K., and Nemoto, K. 1996. Exchange Monte Carlo method and application to spin glass simulations. Journal of the Physical Society of Japan, 65, 1604-1608.
[101] Ito, K. 1993. Encyclopedic Dictionary of Mathematics 2, 2nd edn. Cambridge, MA: MIT Press.
[102] Jacob, W. 1998. Surface reactions during growth and erosion of hydrocarbon films. Thin Solid Films, 326(1-2), 1–42.
[103] Jasa, T., and Xiang, N. 2005. Using nested sampling in the analysis of multi-rate sound energy decay in acoustically coupled rooms. AIP Conference Proceedings, 803(1), 189–196.
[104] Jaynes, E. T., and Bretthorst, L. 2003. Probability Theory, The Logic of Science. Oxford: Oxford University Press.
[105] Jaynes, E. T. 1973. The well-posed problem. Foundations of Physics, 3, 477–493.
[106] Jaynes, E. T. 1968. Prior probabilities. IEEE Transactions on Systems Science and Cybernetics, SSC4, 227–241.
[107] Jeffreys, H. 1961. Theory of Probability. Oxford: Oxford University Press.
[108] Jones, P.D., New, M., Parker, D.E., Martin, S., and Riger, I.G. 1999. Surface air temperature and its variations over the last 150 years. Reviews of Geophysics, 37, 173-199.
[109] Kaelbling, L. P., Littman, M. L., and Moore, A. W. 1996. Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237–285.
[110] Kaelbling, L.P., Littman, M.L., and Cassandra, A. R. 1998. Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101, 99–134.
[111] Kapur, J. N., and Kesavan, H. K. (eds). 1989. Maximum-Entropy Models in Science and Engineering. New York: John Wiley & Sons.
[112] Kapur, J. N., and Kesavan, H. K. 1992. Entropy Optimization Principles with Applications. Boston, MA: Academic Press.
[113] Kass, R.E., and Raftery, A.E. 1995. Bayes factors. Journal of the American Statistical Association, 90(430), 773–795.
[114] Kelly, F. P. 1979. Reversibility and Stochastic Networks. Chichester, UK: John Wiley & Sons.
[115] Kendall, M. G., and Moran, P. A. P. 1963. Geometrical Probability. London: Griffin.
[116] Kennedy, M. C., and O'Hagan, A. 2001. Bayesian calibration of computer models (with discussion). Journal of the Royal Statistical Society, Series B, 63(3), 425–464.
[117] Knuth, K. H., Erner, P. M., and Frasso, S. 2007. Designing intelligent instruments. In Knuth, K.H., Caticha, A., Center, J.L., Giffin, A., and Rodrigues, C. C. (eds), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, vol. 954. Melville, NY: AIP; pp 203–211.
[118] Knuth, K. H., and Skilling, J. 2012. Foundations of inference. Axioms, 1, 38–73.
[119] Koch, D. G.et al. 2010. Kepler mission design, realized photometric performance, and early science. Astrophysical Journal Letters, 713(2), L79-L86.
[120] Kolmogorov, A. N. 1956. Foundations of the Theory of Probability. New York: Chelsea Publishing Company.
[121] Kornejew, P., Hirsch, M., Bindemann, T., Dinklage, A., Dreier, H., and Hartfuss, H. J. 2006. Design of multichannel laser interferometry for W7-X. Review of Scientific Instruments, 77, 10F128.
[122] Landau, L. 1944. On the energy loss of fast particles by ionization. Journal of Physics-USSR, 8, 201.
[123] Laplace, P. S. 1951. A Philosophical Essay on Probabilities (translated into English by F. W. Truscott, and F. L. Emory). New York: Dover Publications; p. 4.
[124] Lehmann, E. L. 1993. The Fisher, Neyman-Pearson theories of testing hypotheses: One theory or two?Journal of the American Statistical Association, 88(424), 1242–1249.
[125] Leonard, T., and Hsu, J. S. J. 1999. Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers. Cambridge: Cambridge University Press.
[126] Lewis, D. W. 1991. Matrix Theory, 3rd edn. Singapore: World Scientific.
[127] Lindley, D. V. 1956. On a measure of the information provided by an experiment. Annals of Mathematical Statistics, 27(4), 986–1005.
[128] Lindley, D. V. 1972. Bayesian statistics - a review. Philadelphia, PA: SIAM.
[129] Linsmeier, C. 1994. Auger electron spectroscopy. Vacuum, 45, 673–690.
[130] Loredo, T. J. 1999. Computational technology for Bayesian inference. In Mehringer, D. M., Plante, R. L., and Roberts, D. A. (eds), ASP Conference Series 172: Astronomical Data Analysis Software and Systems VIII, vol. 8. San Francisco, CA: ASP.
[131] Loredo, T. J. 2004. Bayesian Adaptive Exploration.
[132] Loredo, T. J., and Chernoff, D. F. 2003. Bayesian adaptive exploration. In Feigelson, E. D., and Babu, G. J. (eds), Statistical Challenges in Astronomy. Berlin: SpringerVerlag, pp 57–70.
[133] Loredo, T. J., and Chernoff, D. F. 2003. Bayesian adaptive exploration. In Erickson, G., and Zhai, Y. (eds), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, vol. 707. Melville, NY: AIP; pp 330–346.
[134] Luttrell, S.P. 1985. The use of transinformation in the design of data sampling schemes for inverse problems. Inverse Problems, 1, 199–218.
[135] MacKay, D. J. C. 1992. Information-based objective functions for active data selection. Neural Computation, 4(4), 590–604.
[136] MacKay, D. J. C. 2003. Information Theory, Inference and Learning Algorithms. Cambridge: Cambridge University Press.
[137] Marinari, E. 1996. Optimized Monte Carlo Methods. Lectures at the 1996 Budapest Summer School on Monte Carlo Methods.
[138] Marinari, E., and Parisi, G. 1992. Simulated tempering: A new Monte Carlo scheme. Europhysics Letters, 19, 451–458.
[139] Matthews, R. 1998. Data sleuths go to war. New Scientist, 2135.
[140] Mattuck, R. D. 1976. A Guide to Feynman Diagrams in the Many-Body Problem. New York: McGraw-Hill Book Co.
[141] Meroli, S., Passeri, D., and Servoli, L. 2011. Energy loss measurement for charged particles in very thin silicon layers. Journal of Instrumentation, 6, P06013.
[142] Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., and Teller, E. 1953. Equations of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1091.
[143] Mohr, J.P., and Taylor, B.N. 2005. CODATA recommended values of the fundamental physical constants: 2002. Reviews of Modern Physics, 77, 42–47.
[144] Moller, W., and Besenbacher, F. 1980. A note on the 3He-2D nuclear reaction cross section. Nuclear Instruments and Methods, 168(1), 111–114.
[145] Montgomery, D. C., and Peck, E. A. (eds). 1982. Introduction to Linear Regression Analysis. New York: John Wiley & Sons.
[146] Müller, P. 1999. Simulation based optimal design (with discussion). In Bernardo, J. M., Berger, J. O., Dawid, A. P., and Smith, A. F. M. (eds), Bayesian Statistics, vol. 6. Oxford: Oxford University Press; pp 459–474.
[147] Müller, P., and Parmigiani, G. 1995. Numerical evaluation of information theoretic measures. In Berry, D. A., Chaloner, K. M., and Geweke, J. F. (eds), Bayesian Statistics and Econometrics: Essays in Honor of A. Zellner. New York: John Wiley & Sons; pp 397–406.
[148] Müller, P., Sanso, B., and Delorio, M. 2004. Optimal Bayesian design by inhomo-geneous Markov chain simulation. Journal of the American Statistical Association, 99, 788–798.
[149] Müller, P., Berry, D., Grieve, A., Smith, M., and Krams, M. 2007. Simulation-based sequential Bayesian design. Journal of Statistical Planning and Inference, 137(10), 3140–3150.
[150] Murray, C. D., and Dermott, S. F. 1999. Solar System Dynamics. Cambridge: Cambridge University Press.
[151] Myers, R. H., Khuri, A. I., and Carter, W. H. 1989. Response surface methodology: 1966–1988. Technometrics, 31(2), 137–157.
[152] Neal, R. 1999. Regression and classification using Gaussian process priors. In Bernardo, J. M., Berger, J. O., Dawid, A. P., and Smith, A. F. M. (eds), Bayesian Statistics, vol. 6. Oxford: Oxford University Press; pp 69–95.
[153] Neyman, J., and Pearson, E. S. 1933. On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London, 231A, 289–337.
[154] Ng, A.Y., and Jordan, M.I. 2000. PEGASUS: A policy search method for large MDPs and POMDPs. In Uncertainty in Artificial Intelligence: Proceedings of the Sixteenth Conference. Stanford, CA: Morgan Kaufmann; pp 406–415.
[155] Nussbaumer, A., Bittner, E., and Janke, W. 2008. Make life simple: Unleash the full power of the parallel tempering algorithm. Physical Review Letters, 101, 130603.
[156] Oakley, J. E., and O'Hagan, A. 2004. Probabilistic sensitivity analysis of complex models: A Bayesian approach. Journal of the Royal Statistical Society, Series B, 66(3), 751–769.
[157] O'Hagan, A. 1994. Distribution Theory. Kendall's Advanced Theory of Statistics, vol. 1. London: Edward Arnold.
[158] Padayachee, J., Prozesky, V. M., von der Linden, W., Nkwinika, M. S., and Dose, V. 1999. Bayesian PIXE background subtraction. Nuclear Instrument of Methods B, 150, 129–135.
[159] Papoulis, A., and Pillai, S. U. 2002. Probability, Random Variables and Stochastic Processes. London: McGraw-Hill Europe.
[160] Petz, F. and Hiai, D. 2000. The Semicircle Law, Free Random Variables and Entropy. Mathematical Surveys and Monographs, vol. 77. Providence, RI: American Mathematical Society.
[161] Press, W. H. 1996. Understanding data better with Bayesian and global statistical methods. astro-ph/9604126.
[162] Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P. (eds). 1992. Numerical Recipes, 2nd edn. Cambridge: Cambridge University Press.
[163] Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P. (eds). 2007. Numerical Recipes, 3rd edn. Cambridge: Cambridge University Press.
[164] Press, W. H., Vetterling, W. T., Teukolsky, S.A., and Flannery, B. P. 1992. Numerical Recipes in C, 2nd edn, vol. 1. Cambridge: Cambridge University Press.
[165] Preuss, R., Muramatsu, A., von der Linden, W., Assaad, F. F., Dieterich, P., and Hanke, W. 1994. Spectral properties of the one-dimensional Hubbard model. Physical Review Letters, 73, 732.
[166] Preuss, R., Hanke, W., and von der Linden, W. 1995. Quasiparticle dispersion of the 2D Hubbard model: From an insulator to a metal. Physical Review Letters, 75, 1344.
[167] Preuss, R., Dose, V., and von der Linden, W. 1999. Dimensionally exact form-free energy confinement scaling in W7-AS. Nuclear Fusion, 39, 849–862.
[168] Preuss, R., Dreier, H., Dinklage, A., and Dose, V. 2008. Data adaptive control parameter estimation for scaling laws for magnetic fusion devices. EPL, 81(5), 55001.
[169] Pronzato, L. 2008. Optimal experimental design and some related control problems. Automatica, 44, 303–325.
[170] Protter, P.E. 2000. Stochastic Integration and Differential Equations. 2.1 edn. New York: Springer.
[171] Prozesky, V. M., Padayachee, J., Fischer, R., von der Linden, W., Dose, V. and Ryan, C. G. 1997. The use of maximum entropy and Bayesian techniques in nuclear microprobe applications. Nuclear Instrument and Methods B, 130, 113–117.
[172] Pukelsheim, F. 1993. Optimal Design of Experiments. New York: John Wiley & Sons.
[173] Rodriguez, C. C. 1999. Are we really cruising a hypothesis space. In von der Linden, W., Dose, V., Fischer, R., and Preuss, R. (eds), Maximum Entropy and Bayesian Methods. Dordrecht: Kluwer Academic Publishers; pp 131–140.
[174] Rosenkrantz, R.D. (ed.). 1983. E. T. Jaynes: Papers on Probability, Statistics and Statistical Physics. Dordrecht: Reidel.
[175] Ryan, K. J. 2003. Estimating expected information gains for experimental designs with application to the random fatigue-limit model. Journal of Computational and Graphical Statistics, 12(3), 585–603.
[176] Sacks, J., Welch, W. J., Mitchell, T. J., and Wynn, H. P. 1989. Design and analysis of computer experiments. Statistical Science, 4(4), 409–435.
[177] Salsburg, D. 2002. The Lady Tasting Tea. New York: Henry Hold and Co.
[178] Saltelli, A., Tarantola, S., and Campolongo, F. 2000. Sensitivity analysis as an ingredient of modeling. Statistical Science, 15, 377–395.
[179] Schwarz-Selinger, T., Preuss, R., Dose, V., and von der Linden, W. 2001. Analysis of multicomponent mass spectra applying Bayesian probability theory. Journal of Mass Spectroscopy, 36, 866–874.
[180] Sebastiani, P., and Wynn, H. P. 2000. Maximum entropy sampling and optimal Bayesian experimental design. Journal of the Royal Statistical Society, series B, 62(1), 145–157.
[181] Shaw, J. R., Bridges, M., and Hobson, M. P. 2007. Efficient Bayesian inference for multimodal problems in cosmology. Monthly Notices of the Royal Astronomical Society, 378, 1365–1370.
[182] Shore, J. E., and Johnson, R. W. 1980. Axiomatic derivation of the principle of maximum entropy and the principle and the minimum of crossentropy. IEEE Transactions on Information Theory, 26(26).
[183] Silver, R.N., and Martz, H. F. 1994. Applications of quantum entropy to statistics. In Robinson, H. C. (ed.), Presented at the American Statistical Association, Toronto, Ontario, 14-18 August 1994; pp 14–18.
[184] Silver, R.N., Sivia, D.S., and Gubernatis, J.E. 1990. Application of maxent and Bayesian methods to many-body physics. Physical Review B, 41, 2380.
[185] Silverman, B. W. 1986. Density Estimation for Statistics and Data Analysis. London: Chapman and Hall.
[186] Singh, V. P. 2013. Entropy Theory and its Application in Environmental and Water Engineering. Oxford: Wiley-Blackwell.
[187] Sivia, D. S., and David, W. I. F. 1994. A Bayesian approach to extracting structure-factor amplitudes from powder diffraction data. Acta Crystallographical A, 50, 703–714.
[188] Sivia, D. S. and Skilling, J. 2006. Data Analysis: A Bayesian Tutorial. Oxford: Oxford University Press.
[189] Skilling, J. 1990. Quantified maximum entropy. In Fougère, P.F. (ed.), Maximum Entropy and Bayesian Methods. Dordrecht: Kluwer Academic Publishers; p. 341.
[190] Skilling, J. 1991. Fundamentals of MaxEnt in data analysis. In Buck, B., and Macaulay, V. A. (eds), Maximum Entropy in Action. Oxford: Clarendon Press; p. 19.
[191] Skilling, J. 2004. Nested sampling. In Erickson, G., Rychert, J. T., and Smith, C. R. (eds), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, vol. 735. Melville, NY: AIP; pp 395–405.
[192] Skilling, J. 2006. Nested sampling for general Bayesian computation. Bayesian Analysis, 1(4), 833–859.
[193] Skilling, J. 2009. Nested sampling's convergence. In Bayesian Inference and Maximum Entropy Methods in Science and Engineering: The 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP Conference Proceedings, vol. 1193; pp 277–291.
[194] Skilling, J., and Gull, S. F. 1991. Bayesian Maximum Entropy Image Reconstruction. Spatial Statistics and Imaging, vol. 20. Institute of Mathematical Statistics.
[195] Skinner, C.H., Haasz, A.A., Alimov, V.Kh., Bekris, N., Causey, R.A., Clark, R. E. H., Coad, J. P., Davis, J. W., Doerner, R. P., Mayer, M., Pisarev, A., Roth, J., and Tanabe, T. 2008. Recent advances on hydrogen retention in ITER's plasma facing materials: Beryllium, carbon, and tungsten. Fusion Science and Technology, 54(4), 891–945.
[196] Stanley, R. P. 2011. Enumerative Combinatorics, vol. I, 2nd edn. Cambridge: Cambridge University Press.
[197] Steinerg, D. M., and Hunter, W. G. 1984. Experimental design: Review and comment. Technometrics, 26(2), 71–97.
[198] Stone, M. 1959. Application of a measure of information to the design and comparison of regression experiment. Annuals of Mathematical Statistics, 30, 55–70.
[199] Strauss, C.E.M., Wolpert, D.H., and Wolf, D.R. 1993. Alpha, evidence, and the entropic prior. In Heidbreder, G. (ed.), Maximum Entropy and Bayesian Methods. Dordrecht: Kluwer Academic Publishers.
[200] Stroth, U., Murakami, M., Dory, R. A., Yamada, H., Okamura, S., Sano, F., and Obiki, T. 1996. Energy confinement scaling from the international stellarator database. Nuclear Fusion, 36(8), 1063–1078.
[201] Tesmer, J., and Nastasi, M. (eds). 1995. Handbook of Modern Ion Beam Analysis. Pittsburgh, PA: Material Research Society.
[202] Thrun, S., and Fox, D. 2005. Probabilistic Robotics. Boston, MA: MIT Press.
[203] Titterington, D. M. 1985. General structure of regularization procedures in image reconstruction. Astronomy and Astrophysics, 144, 381–387.
[204] Toman, B. 1996. Bayesian experimental design for multiple hypothesis testing. Journal of the American Statistical Association, 91(433), 185–190.
[205] Toman, B. 1999. Bayesian experimental design. In Kotz, S., and Johnson, N. L. (eds), Encyclopedia of statistical sciences, Update vol. 3. New York: John Wiley & Sons; pp 35–39.
[206] Tribus, M. 1969. Rational Descriptions, Decisions and Design. New York: Pergamon.
[207] von der Linden, W. 1995. Maximum-entropy data analysis. Applied Physics A, 60, 155.
[208] von der Linden, W., Donath, M., and Dose, V. 1993. Unbiased access to exchange splitting of magnetic bands using the maximum entropy method. Physical Review Letters, 71, 899.
[209] von der Linden, W., Dose, V., Matzdorf, R., Pantforder, A., Meister, G., and Goldmann, A. 1997. Improved resolution in HREELS using maximum-entropy deconvolution: CO on PtxNi1_x(111). Journal of Electron Spectroscopy and Related Phenomena, 83, 1–7.
[210] von der Linden, W., Dose, V., Memmel, N., and Fischer, R. 1998. Probabilistic evaluation of growth models based on time dependent Auger signals. Surface Science, 409, 290–301.
[211] von der Linden, W., Dose, V., Padayachee, J., and Prozesky, V. 1999. Signal and background separation. Physical Review E, 59, 6527–6534.
[212] von der Linden, W. 1992. A QMC approach to many-body physics. Physics Reports, 220, 55.
[213] von Neumann, J. 1951. Various techniques used in connection with random digits. Monte Carlo methods. National Bureau of Standards, 12, 36–38.
[214] von Toussaint, U. 2011. Bayesian inference in physics. Review of Modern Physics, 11(3), 943–999.
[215] von Toussaint, U., Gori, S., and Dose, V. 2004. Bayesian neural-networks-based evaluation of binary speckle data. Applied Optics, 43, 5356–5363.
[216] von Toussaint, U., Schwarz-Selinger, T., and Gori, S. 2008. Optimizing nuclear reaction analysis (NRA) using Bayesian experimental design. In Lauretto, M. S., Pereira, C. A. B., and Stern, J. M. (eds), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, vol. 1073; Melville, NY: AIP. pp 348–358.
[217] Werman, M., and Keren, D. 2001. A Bayesian method for fitting parametric and nonparametric models to noisy data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 528–534.
[218] Woess, W. (ed.). 2009. Denumerable Markov Chains, vol. 1. European Mathematical Society Publishing House.
[219] Wright, J. T., and Howard, A. W. 2009. Efficient fitting of multiplanet Keplerian models to radial velocity and astrometry data. Astrophysical Journal Supplement, 182(1), 205–215.
