References

References#

[Aka73]

Hirotugu Akaike. Information theory and an extension of the maximum likelihood principle. In 2nd International Symposium on Information Theory, 267–281. 1973.

[AM02]

Steven P. Asprey and Sandro Macchietto. Designing robust optimal dynamic experiments. Journal of Process Control, 12(4):545–556, 2002. doi:10.1016/S0959-1524(01)00020-8.

[ADT07]

Anthony Atkinson, Alexander Donev, and Randall Tobias. Optimum Experimental Designs, with SAS. Oxford University Press, 2007.

[ABB98]

Anthony C. Atkinson, Barbara Bogacka, and Marek B. Bogacki. D- and T-optimum designs for the kinetics of a reversible chemical reaction. Chemometrics and Intelligent Laboratory Systems, 43(1–2):185–198, 1998. doi:10.1016/S0169-7439(98)00046-X.

[BKW80]

David A. Belsley, Edwin Kuh, and Roy E. Welsch. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Wiley, 1980. ISBN 0-471-05856-4.

[BB60]

George E. P. Box and Donald W. Behnken. Some new three level designs for the study of quantitative variables. Technometrics, 2(4):455–475, 1960. doi:10.1080/00401706.1960.10489912.

[BHH05]

George E. P. Box, J. Stuart Hunter, and William G. Hunter. Statistics for Experimenters: Design, Innovation, and Discovery. Wiley-Interscience, 2nd edition, 2005.

[BW51]

George E. P. Box and Kenneth B. Wilson. On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1):1–45, 1951.

[BRKunsch01]

Roland Brun, Peter Reichert, and Hans R. Künsch. Practical identifiability analysis of large environmental simulation models. Water Resources Research, 37(4):1015–1030, 2001. doi:10.1029/2000WR900350.

[BFFEH84]

Guido Buzzi-Ferraris, Pio Forzatti, Gerhard Emig, and Hartmut Hofmann. Sequential experimental design for model discrimination in the case of multiple responses. Chemical Engineering Science, 39(1):81–85, 1984. doi:10.1016/0009-2509(84)80132-3.

[CH07]

Yunfei Chu and Juergen Hahn. Parameter set selection for estimation of nonlinear dynamic systems. AIChE Journal, 53(11):2858–2870, 2007. doi:10.1002/aic.11295.

[CH12]

Yunfei Chu and Juergen Hahn. Generalization of a parameter set selection procedure based on orthogonal projections and the D-optimality criterion. AIChE Journal, 58(7):2085–2096, 2012. doi:10.1002/aic.12727.

[CC57]

William G. Cochran and Gertrude M. Cox. Experimental Designs. Wiley, New York, 2nd edition, 1957.

[Cor02]

John A. Cornell. Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data. Wiley, 3rd edition, 2002.

[DCC01]

Enrique Del Castillo and Suntara Cahya. A tool for computing confidence regions on the stationary point of a response surface. The American Statistician, 55(4):358–365, 2001. doi:10.1198/000313001753272349.

[DS80]

George Derringer and Ronald Suich. Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4):214–219, 1980. doi:10.1080/00224065.1980.11980968.

[Fis35]

Ronald A. Fisher. The Design of Experiments. Oliver and Boyd, Edinburgh, 1935.

[FJB+19]

Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, and Noah Goodman. Variational Bayesian optimal experimental design. In Advances in Neural Information Processing Systems 32. 2019.

[FM08a]

Gaia Franceschini and Sandro Macchietto. Model-based design of experiments for parameter precision: state of the art. Chemical Engineering Science, 63(19):4846–4872, 2008. doi:10.1016/j.ces.2007.11.034.

[FM08b]

Gaia Franceschini and Sandro Macchietto. Model-based design of experiments for parameter precision: state of the art. Chemical Engineering Science, 63(19):4846–4872, 2008. doi:10.1016/j.ces.2007.11.034.

[GBB09]

Federico Galvanin, Massimiliano Barolo, and Fabrizio Bezzo. Online model-based redesign of experiments for parameter estimation in dynamic systems. Industrial & Engineering Chemistry Research, 48(9):4415–4427, 2009. doi:10.1021/ie8018356.

[GMB07]

Federico Galvanin, Sandro Macchietto, and Fabrizio Bezzo. Model-based design of parallel experiments. Industrial & Engineering Chemistry Research, 46(3):871–882, 2007. doi:10.1021/ie0611406.

[GMB26]

Marco Geremia, Sandro Macchietto, and Fabrizio Bezzo. A review on model-based design of experiments for parameter precision: open challenges, trends and future perspectives. Chemical Engineering Science, 319:122347, 2026.

[GonzalezZ23]

Leonardo D. González and Victor M. Zavala. New paradigms for exploiting parallel experiments in Bayesian optimization. Computers & Chemical Engineering, 170:108110, 2023. doi:10.1016/j.compchemeng.2022.108110.

[GWC+07]

Ryan N. Gutenkunst, Joshua J. Waterfall, Fergal P. Casey, Kevin S. Brown, Christopher R. Myers, and James P. Sethna. Universally sloppy parameter sensitivities in systems biology models. PLoS Computational Biology, 3(10):e189, 2007. doi:10.1371/journal.pcbi.0030189.

[HR65]

William G. Hunter and Anthony M. Reiner. Designs for discriminating between two rival models. Technometrics, 7(3):307–323, 1965. doi:10.1080/00401706.1965.10490265.

[JN09]

Bradley Jones and Christopher J. Nachtsheim. Split-plot designs: what, why, and how. Journal of Quality Technology, 41(4):340–361, 2009. doi:10.1080/00224065.2009.11917790.

[JN11]

Bradley Jones and Christopher J. Nachtsheim. A class of three-level designs for definitive screening in the presence of second-order effects. Journal of Quality Technology, 43(1):1–15, 2011. doi:10.1080/00224065.2011.11917841.

[JSW98]

Donald R. Jones, Matthias Schonlau, and William J. Welch. Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 13(4):455–492, 1998.

[KW59]

Jack Kiefer and Jacob Wolfowitz. Optimum designs in regression problems. The Annals of Mathematical Statistics, 30(2):271–294, 1959. doi:10.1214/aoms/1177706252.

[KRKT13]

Clemens Kreutz, Andreas Raue, Daniel Kaschek, and Jens Timmer. Profile likelihood in systems biology. FEBS Journal, 280(11):2564–2571, 2013. doi:10.1111/febs.12276.

[Len89]

Russell V. Lenth. Quick and easy analysis of unreplicated factorials. Technometrics, 31(4):469–473, 1989. doi:10.1080/00401706.1989.10488595.

[Lin56]

Dennis V. Lindley. On a measure of the information provided by an experiment. Annals of Mathematical Statistics, 27(4):986–1005, 1956. doi:10.1214/aoms/1177728069.

[MBC79]

Michael D. McKay, Richard J. Beckman, and William J. Conover. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2):239–245, 1979. doi:10.2307/1268522.

[MA66]

R. A. McLean and V. L. Anderson. Extreme vertices design of mixture experiments. Technometrics, 8(3):447–454, 1966. doi:10.1080/00401706.1966.10490377.

[MXPW11]

Hongyu Miao, Xiaohua Xia, Alan S. Perelson, and Hulin Wu. On identifiability of nonlinear ode models and applications in viral dynamics. SIAM Review, 53(1):3–39, 2011. Canonical review of structural and practical identifiability. doi:10.1137/090757009.

[Mon17]

Douglas C. Montgomery. Design and Analysis of Experiments. Wiley, Hoboken, NJ, 9th edition, 2017.

[MM95]

Max D. Morris and Toby J. Mitchell. Exploratory designs for computational experiments. Journal of Statistical Planning and Inference, 43(3):381–402, 1995. doi:10.1016/0378-3758(94)00035-T.

[ODM19]

Simon Olofsson, Marc P. Deisenroth, and Ruth Misener. GPdoemd: a Python package for design of experiments for model discrimination. Computers & Chemical Engineering, 125:54–70, 2019. doi:10.1016/j.compchemeng.2019.03.010.

[PB46]

R. L. Plackett and J. P. Burman. The design of optimum multifactorial experiments. Biometrika, 33(4):305–325, 1946.

[RFIBS24]

Tom Rainforth, Adam Foster, Desi R. Ivanova, and Freddie Bickford Smith. Modern Bayesian experimental design. Statistical Science, 2024. arXiv:2302.14545.

[RW06]

Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA, 2006.

[RKM+09]

A. Raue, C. Kreutz, T. Maiwald, J. Bachmann, M. Schilling, U. Klingmüller, and J. Timmer. Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics, 25(15):1923–1929, 2009. doi:10.1093/bioinformatics/btp358.

[SPC25]

Marco Sandrin, Constantinos C. Pantelides, and Beno\^ıt Chachuat. Methodological and computational framework for model-based design of parallel experiment campaigns under uncertainty. Journal of Process Control, 152:103465, 2025. doi:10.1016/j.jprocont.2025.103465.

[Scheffe58]

Henry Scheffé. Experiments with mixtures. Journal of the Royal Statistical Society. Series B (Methodological), 20(2):344–360, 1958.

[Scheffe63]

Henry Scheffé. The simplex-centroid design for experiments with mixtures. Journal of the Royal Statistical Society. Series B (Methodological), 25(2):235–263, 1963.

[Sch78]

Gideon Schwarz. Estimating the dimension of a model. Annals of Statistics, 6(2):461–464, 1978. doi:10.1214/aos/1176344136.

[SLA12]

Jasper Snoek, Hugo Larochelle, and Ryan P. Adams. Practical Bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems 25, 2951–2959. 2012.

[Vil19]

Alejandro F. Villaverde. Observability and structural identifiability of nonlinear biological systems. Complexity, 2019:8497093, 2019. Tutorial survey of structural identifiability from the differential-geometric viewpoint. doi:10.1155/2019/8497093.

[Vuo89]

Quang H. Vuong. Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57(2):307–333, 1989. doi:10.2307/1912557.

[WD22]

Jialu Wang and Alexander W. Dowling. Pyomo.DOE: an open-source package for model-based design of experiments in Python. AIChE Journal, 68(12):e17813, 2022. doi:10.1002/aic.17813.

[YSK+03]

Kunhao Z. Yao, Beth M. Shaw, Bing Kou, Kimberley B. McAuley, and D. W. Bacon. Modeling ethylene/butene copolymerization with multisite catalysts: parameter estimability and experimental design. Polymer Reaction Engineering, 11(3):563–588, 2003. doi:10.1081/PRE-120024426.

[ZACM03]

Alyaa Zahran, Christine M. Anderson-Cook, and Raymond H. Myers. Fraction of design space to assess prediction capability of response surface designs. Journal of Quality Technology, 35(4):377–386, 2003. doi:10.1080/00224065.2003.11980235.