[1] Abramowitz, M., and Stegun, I.A. 1965. Handbook of Mathematical Functions. Washington DC: National Bureau of Standards.Google Scholar

[2] Akaike, H. 1974. A new look at statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[5] Amsel, G., and Lanford, W. A. 1984. Nuclear reaction techniques in materials analysis. Annual Review Nuclear and Particle Science, 34, 435–460.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[7] Atkinson, A. C., Donev, A., and Tobias, R. 2007. Optimum Experimental Designs, with SAS. Oxford: Oxford University Press.Google Scholar

[8] Bailey, N.T. J. 1990. The Elements of Stochastic Processes. New York: John Wiley & Sons.Google Scholar

[9] Ballentine, L. E. 2003. Quantum Mechanics: A Modern Development. Singapore: World Scientific Publishing.Google Scholar

[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.Google Scholar

[11] Bellman, R. E. 1957. Dynamic Programming. Princeton, NJ: Princeton University Press.Google ScholarPubMed

[12] Berger, J. O. 1985. Statistical Decision Theory and Bayesian Analysis. New York: Springer.CrossRefGoogle Scholar

[13] Berger, J. O., and Wolpert, R. L. 1988. The Likelihood Principle, 2nd edn. Hayward, CA: IMS.Google Scholar

[14] Bernardo, J. M. 1979. Expected information as expected utility. Annals of Statistics, 7(3), 686–690.CrossRefGoogle Scholar

[15] Bernardo, J.M., and Smith, A.F.M. 2000. Bayesian Theory. Chichester, UK: John Wiley & Sons.Google Scholar

[16] Binder, K. 1986. Monte Carlo Methods in Statistical Physics, 2nd edn. Topics in Current Physics, vol. 1. Berlin: Springer-Verlag.CrossRefGoogle Scholar

[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.Google Scholar

[18] Bremaud, P. 1999. Markov Chains, Gibbs Fields, Monte Carlo Simulation, and Queues. New York: Springer.Google Scholar

[19] Bretthorst, G. L. 1988. Bayesian Spectrum Analysis and Parameter Estimation. New York: Springer.CrossRefGoogle Scholar

[20] Bretthorst, G. L. 1990. Bayesian analysis I. Parameter estimation using quadrature NMR models. Journal of Magnetic Resonance, 88, 533–551.Google Scholar

[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.Google Scholar

[22] Bretthorst, G. L. (ed.). 1988. Bayesian Spectrum Analysis and Parameter Estimation. Berlin: Springer-Verlag.CrossRef

[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.CrossRefGoogle Scholar

[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.Google Scholar

[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.CrossRefGoogle Scholar

[26] Bryson, B. 2010. At Home: A Short History of Private Life. London: Transworld Publishers.Google Scholar

[27] Buck, B., and Macaulay, V. A. (eds). 1991. Maximum Entropy in Action. Oxford: Clarendon Press.Google Scholar

[28] Chaloner, K., and Verdinelli, I. 1995. Bayesian experimental design: A review. Statistical Science, 10, 273–304.CrossRefGoogle Scholar

[29] Chernoff, H. 1972. Sequential Analysis and Optimal Design. Philadelphia, PA: SIAM.CrossRefGoogle Scholar

[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.Google Scholar

[31] Chopin, N., and Robert, C.P. 2010. Properties of nested sampling. Biometrika, 97(3), 741–755.CrossRefGoogle Scholar

[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.Google Scholar

[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.CrossRefGoogle Scholar

[34] Cornu, A., and Massot, R. 1979. Compilation of Mass Spectral Data. London: Heyden.Google Scholar

[35] Cox, R. T. 1946. Probability, frequency and reasonable expectation. American Journal of Physics, 14, 1–13.CrossRefGoogle Scholar

[36] Cox, R. T. 1961. The Algebra of Probable Inference. Baltimore, MD: Johns Hopkins Press.Google Scholar

[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.CrossRefGoogle Scholar

[38] Daghofer, M., and von der Linden, W. 2004. Perfect Tempering. AIP Conference Proceedings, vol. 735, p. 355.Google Scholar

[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.Google Scholar

[40] DeGroot, M. H. 1962. Uncertainty, information and sequential experiments. Annals of Mathematical Statistics, 33(2), 404–419.CrossRefGoogle Scholar

[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.Google Scholar

[42] Devroye, L. 1986. Non-uniform Random Variate Generation. New York: Springer.CrossRefGoogle Scholar

[43] Dittrich, W., and Reuter, M. 2001. Classical and Quantum Dynamics. New York: Springer.CrossRefGoogle Scholar

[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.Google Scholar

[45] Dobb, J. L. 1953. Stochastic Processes. New York: John Wiley & Sons.Google Scholar

[46] Doetsch, G. (ed.). 1976. Einführung und Anwendung der Laplace-Transformation. Basel: Birkhauser-Verlag.CrossRef

[47] Dose, V. 2007. Bayesian estimate of the Newtonian constant of gravitation. Measurement Science and Technology, 18, 176–182.Google Scholar

[48] Dose, V. 2009. Analysis of rare-event time series with application to Caribbean hurricane data. Europhysics Letters, 85, 59001.CrossRefGoogle Scholar

[49] Dose, V., and Menzel, A. 2004. Bayesian analysis of climate change impacts in phenology. Global Change Biology, 10, 259–272.CrossRefGoogle Scholar

[50] Dose, V., Fauster, Th., and Gossmann, H.J. 1981. The inversion of autoconvolution integrals. Journal of Computational Physics, 41(1), 34–50.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[55] Dreier, H., Dinklage, A., Fischer, R., Hirsch, M., and Kornejew, P. 2006. Bayesian design of plasma diagnostics. Review of Scientific Instruments, 77, 10F323.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[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.Google Scholar

[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.CrossRefGoogle Scholar

[59] Economou, E.N. 1979. Green's Functions in Quantum Physics. Springer Series in Solid State Physics, vol. 1. Berlin: Springer-Verlag.CrossRefGoogle Scholar

[60] Edwards, W., Lindman, H., and Savage, L. J. 1963. Bayesian statistical inference for psychological research. Psychological Review, 70, 193–242.CrossRefGoogle Scholar

[61] Elstrodt, J. 1999. Maß- und Integrationstheorie (Das Lebesgue-Integral, S. 118160.). 2. edn. Heidelberg: Springer.CrossRefGoogle Scholar

[62] Emsh, G.G., Eggerfeldt, G.C., and Streit, L. (eds). 1994. On Klauder's Path: A Field Trip. Singapore: World Scientific.Google Scholar

[63] Enzensberger, H. M. 2011. Fatal Numbers: Why Count on Chance. New York: Upper Westside Philosophers.Google Scholar

[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.CrossRefGoogle Scholar

[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.Google Scholar

[66] Fedorov, V. V. 1972. Theory of Optimal Experiments. New York: Academic Press.Google Scholar

[67] Feller, W. 1968. An Introduction to Probability Theory and Applications I and II. New York: John Wiley & Sons.Google Scholar

[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.Google Scholar

[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.CrossRefGoogle Scholar

[70] Fischer, R., Hanson, K. M., Dose, V., and von der Linden, W. 2000. Background estimation in experimental spectra. Physical Review E, 61, 1152.CrossRefGoogle ScholarPubMed

[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.Google Scholar

[72] Fisher, R. A. 1925. Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.Google Scholar

[73] Fisher, R.A. 1990. Statistical Methods and Scientific Inference. Oxford: Oxford University Press.Google Scholar

[74] Fishman, G. S. 1996. Monte Carlo: Concepts, algorithms and applications. Berlin: Springer-Verlag.CrossRefGoogle Scholar

[75] Freedman, D., Pisani, R., and Purves, R. 2007. Statistics, 4th edn. New York: W. W. Norton & Co.Google Scholar

[76] Frieden, B. R. 1991. Probability, Statistical Optics, and Data Testing. Springer Series in Information Sciences, no. 10. Berlin: Springer-Verlag.CrossRefGoogle Scholar

[77] Frieden, R., and Gatenby, R.A. 2007. Exploratory Data Analysis Using Fisher Information. New York: Springer.CrossRefGoogle Scholar

[78] Frohner, F. H. 2000. Evaluation and Analysis of Nuclear Resonance Data, JEFF Report 18. Paris: OECD Nuclear Energy Agency.Google Scholar

[79] Fukuda, Y. 2010. Appearance potential spectroscopy (APS): Old method, but applicable to study nano-structures. Analytical Sciences, 26, 187–197.CrossRefGoogle ScholarPubMed

[80] Garrett, A. J.M. 1991. Ockham's razor. Physics World, May, 39.CrossRefGoogle Scholar

[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.Google Scholar

[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.Google Scholar

[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.CrossRefGoogle Scholar

[84] Gilks, W.R., Richardson, S., and Spiegelhalter, D.J. 1997. Markov Chain Monte CarloinPractice. London: Chapman Hall.Google Scholar

[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.Google Scholar

[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.Google Scholar

[87] Goodman, S.N. 1999. Toward evidence-based medical statistics. 1: The P value fallacy. Annals of Internal Medicine, 130(12), 995–1004.Google Scholar

[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.Google Scholar

[89] Grandy, W. T. 1987. Foundations of Statistical Mechanics I, II. Amsterdam: Kluwer Academic Publishers.CrossRefGoogle Scholar

[90] Gregory, P. 2005. Bayesian Logical Data Analysis for the Physical Sciences. Cambridge: Cambridge University Press.CrossRefGoogle Scholar

[91] Grinstead, C. M., and Snell, J. L. 1998. Introduction to Probability. Providence, RI: American Mathematical Society.Google Scholar

[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.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[94] Gull, S. F., and Daniell, G. J. 1978. Image reconstruction from incomplete and noisy data. Nature, 272, 686.CrossRefGoogle Scholar

[95] Gull, S. F., and Skilling, J. 1984. Maximum entropy method in image processing. IEEE Proceedings, 131 F, 646.Google Scholar

[96] Hammersley, H. 1965. Monte Carlo Methods. London: Methiens Monographs.Google Scholar

[97] Harney, H. L. 2003. Bayesian Inference: Parameter Estimation and Decisions. Berlin: Springer-Verlag.CrossRefGoogle Scholar

[98] Hjalmarsson, H. 2005. From experiment design to closed-loop control. Automatica, 41, 393–438.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[101] Ito, K. 1993. Encyclopedic Dictionary of Mathematics 2, 2nd edn. Cambridge, MA: MIT Press.Google Scholar

[102] Jacob, W. 1998. Surface reactions during growth and erosion of hydrocarbon films. Thin Solid Films, 326(1-2), 1–42.CrossRefGoogle Scholar

[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.Google Scholar

[104] Jaynes, E. T., and Bretthorst, L. 2003. Probability Theory, The Logic of Science. Oxford: Oxford University Press.CrossRefGoogle Scholar

[105] Jaynes, E. T. 1973. The well-posed problem. Foundations of Physics, 3, 477–493.CrossRefGoogle Scholar

[106] Jaynes, E. T. 1968. Prior probabilities. IEEE Transactions on Systems Science and Cybernetics, SSC4, 227–241.Google Scholar

[107] Jeffreys, H. 1961. Theory of Probability. Oxford: Oxford University Press.Google Scholar

[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.CrossRefGoogle Scholar

[109] Kaelbling, L. P., Littman, M. L., and Moore, A. W. 1996. Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237–285.Google Scholar

[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.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[113] Kass, R.E., and Raftery, A.E. 1995. Bayes factors. Journal of the American Statistical Association, 90(430), 773–795.CrossRefGoogle Scholar

[114] Kelly, F. P. 1979. Reversibility and Stochastic Networks. Chichester, UK: John Wiley & Sons.Google Scholar

[115] Kendall, M. G., and Moran, P. A. P. 1963. Geometrical Probability. London: Griffin.Google Scholar

[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.CrossRefGoogle Scholar

[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.Google Scholar

[118] Knuth, K. H., and Skilling, J. 2012. Foundations of inference. Axioms, 1, 38–73.CrossRefGoogle Scholar

[119] Koch, D. G.et al. 2010. Kepler mission design, realized photometric performance, and early science. Astrophysical Journal Letters, 713(2), L79-L86.CrossRefGoogle Scholar

[120] Kolmogorov, A. N. 1956. Foundations of the Theory of Probability. New York: Chelsea Publishing Company.Google Scholar

[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.CrossRefGoogle Scholar

[122] Landau, L. 1944. On the energy loss of fast particles by ionization. Journal of Physics-USSR, 8, 201.Google Scholar

[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.Google Scholar

[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.CrossRefGoogle Scholar

[125] Leonard, T., and Hsu, J. S. J. 1999. Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers. Cambridge: Cambridge University Press.Google Scholar

[126] Lewis, D. W. 1991. Matrix Theory, 3rd edn. Singapore: World Scientific.CrossRefGoogle Scholar

[127] Lindley, D. V. 1956. On a measure of the information provided by an experiment. Annals of Mathematical Statistics, 27(4), 986–1005.CrossRefGoogle Scholar

[128] Lindley, D. V. 1972. Bayesian statistics - a review. Philadelphia, PA: SIAM.CrossRefGoogle Scholar

[129] Linsmeier, C. 1994. Auger electron spectroscopy. Vacuum, 45, 673–690.CrossRefGoogle Scholar

[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.Google Scholar

[131] Loredo, T. J. 2004. Bayesian Adaptive Exploration.CrossRefGoogle Scholar

[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.Google Scholar

[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.Google Scholar

[134] Luttrell, S.P. 1985. The use of transinformation in the design of data sampling schemes for inverse problems. Inverse Problems, 1, 199–218.CrossRefGoogle Scholar

[135] MacKay, D. J. C. 1992. Information-based objective functions for active data selection. Neural Computation, 4(4), 590–604.CrossRefGoogle Scholar

[136] MacKay, D. J. C. 2003. Information Theory, Inference and Learning Algorithms. Cambridge: Cambridge University Press.Google Scholar

[137] Marinari, E. 1996. Optimized Monte Carlo Methods. Lectures at the 1996 Budapest Summer School on Monte Carlo Methods.Google Scholar

[138] Marinari, E., and Parisi, G. 1992. Simulated tempering: A new Monte Carlo scheme. Europhysics Letters, 19, 451–458.CrossRefGoogle Scholar

[139] Matthews, R. 1998. Data sleuths go to war. New Scientist, 2135.Google Scholar

[140] Mattuck, R. D. 1976. A Guide to Feynman Diagrams in the Many-Body Problem. New York: McGraw-Hill Book Co.Google Scholar

[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.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[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.Google Scholar

[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.Google Scholar

[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.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[150] Murray, C. D., and Dermott, S. F. 1999. Solar System Dynamics. Cambridge: Cambridge University Press.Google Scholar

[151] Myers, R. H., Khuri, A. I., and Carter, W. H. 1989. Response surface methodology: 1966–1988. Technometrics, 31(2), 137–157.CrossRefGoogle Scholar

[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.Google Scholar

[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.Google Scholar

[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.Google Scholar

[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.Google Scholar

[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.CrossRefGoogle Scholar

[157] O'Hagan, A. 1994. Distribution Theory. Kendall's Advanced Theory of Statistics, vol. 1. London: Edward Arnold.Google Scholar

[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.CrossRefGoogle Scholar

[159] Papoulis, A., and Pillai, S. U. 2002. Probability, Random Variables and Stochastic Processes. London: McGraw-Hill Europe.Google Scholar

[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.Google Scholar

[161] Press, W. H. 1996. Understanding data better with Bayesian and global statistical methods. astro-ph/9604126.Google Scholar

[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.Google Scholar

[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.CrossRefGoogle ScholarPubMed

[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.CrossRefGoogle ScholarPubMed

[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.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[169] Pronzato, L. 2008. Optimal experimental design and some related control problems. Automatica, 44, 303–325.CrossRefGoogle Scholar

[170] Protter, P.E. 2000. Stochastic Integration and Differential Equations. 2.1 edn. New York: Springer.Google Scholar

[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.CrossRefGoogle Scholar

[172] Pukelsheim, F. 1993. Optimal Design of Experiments. New York: John Wiley & Sons.Google Scholar

[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.Google Scholar

[174] Rosenkrantz, R.D. (ed.). 1983. E. T. Jaynes: Papers on Probability, Statistics and Statistical Physics. Dordrecht: Reidel.CrossRef

[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.CrossRefGoogle Scholar

[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.Google Scholar

[177] Salsburg, D. 2002. The Lady Tasting Tea. New York: Henry Hold and Co.Google Scholar

[178] Saltelli, A., Tarantola, S., and Campolongo, F. 2000. Sensitivity analysis as an ingredient of modeling. Statistical Science, 15, 377–395.Google Scholar

[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.Google ScholarPubMed

[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.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[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).CrossRefGoogle Scholar

[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.Google Scholar

[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.Google Scholar

[185] Silverman, B. W. 1986. Density Estimation for Statistics and Data Analysis. London: Chapman and Hall.CrossRefGoogle Scholar

[186] Singh, V. P. 2013. Entropy Theory and its Application in Environmental and Water Engineering. Oxford: Wiley-Blackwell.CrossRefGoogle Scholar

[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.Google Scholar

[188] Sivia, D. S. and Skilling, J. 2006. Data Analysis: A Bayesian Tutorial. Oxford: Oxford University Press.Google Scholar

[189] Skilling, J. 1990. Quantified maximum entropy. In Fougère, P.F. (ed.), Maximum Entropy and Bayesian Methods. Dordrecht: Kluwer Academic Publishers; p. 341.CrossRefGoogle Scholar

[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.Google Scholar

[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.Google Scholar

[192] Skilling, J. 2006. Nested sampling for general Bayesian computation. Bayesian Analysis, 1(4), 833–859.CrossRefGoogle Scholar

[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.Google Scholar

[194] Skilling, J., and Gull, S. F. 1991. Bayesian Maximum Entropy Image Reconstruction. Spatial Statistics and Imaging, vol. 20. Institute of Mathematical Statistics.Google Scholar

[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.CrossRefGoogle Scholar

[196] Stanley, R. P. 2011. Enumerative Combinatorics, vol. I, 2nd edn. Cambridge: Cambridge University Press.CrossRefGoogle Scholar

[197] Steinerg, D. M., and Hunter, W. G. 1984. Experimental design: Review and comment. Technometrics, 26(2), 71–97.Google Scholar

[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.CrossRefGoogle Scholar

[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.Google Scholar

[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.CrossRefGoogle Scholar

[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.Google Scholar

[203] Titterington, D. M. 1985. General structure of regularization procedures in image reconstruction. Astronomy and Astrophysics, 144, 381–387.Google Scholar

[204] Toman, B. 1996. Bayesian experimental design for multiple hypothesis testing. Journal of the American Statistical Association, 91(433), 185–190.CrossRefGoogle Scholar

[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.Google Scholar

[206] Tribus, M. 1969. Rational Descriptions, Decisions and Design. New York: Pergamon.Google Scholar

[207] von der Linden, W. 1995. Maximum-entropy data analysis. Applied Physics A, 60, 155.CrossRefGoogle Scholar

[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.CrossRefGoogle ScholarPubMed

[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.CrossRefGoogle Scholar

[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.CrossRefGoogle Scholar

[211] von der Linden, W., Dose, V., Padayachee, J., and Prozesky, V. 1999. Signal and background separation. Physical Review E, 59, 6527–6534.CrossRefGoogle ScholarPubMed

[212] von der Linden, W. 1992. A QMC approach to many-body physics. Physics Reports, 220, 55.CrossRefGoogle Scholar

[213] von Neumann, J. 1951. Various techniques used in connection with random digits. Monte Carlo methods. National Bureau of Standards, 12, 36–38.Google Scholar

[214] von Toussaint, U. 2011. Bayesian inference in physics. Review of Modern Physics, 11(3), 943–999.Google Scholar

[215] von Toussaint, U., Gori, S., and Dose, V. 2004. Bayesian neural-networks-based evaluation of binary speckle data. Applied Optics, 43, 5356–5363.Google Scholar

[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.Google Scholar

[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.CrossRefGoogle Scholar

[218] Woess, W. (ed.). 2009. Denumerable Markov Chains, vol. 1. European Mathematical Society Publishing House.CrossRef

[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.CrossRefGoogle Scholar