Fetching the paper…
Reading the bibliography…
Bayesian experimental design (BED) is a framework that uses statistical models and decision making under uncertainty to optimise the cost and performance of a scientific experiment.
“On information and sufficiency.”
Kullback, S. and Leibler, R. A. (1951) · 1951
Earlier work this paper cites.
“Stock and Recruitment.”
Ricker, W. E. (1954) · 1954
Earlier work this paper cites.
“Frequency-dependent selection in vaccine-associated pneumococcal population dynamics.”
Corander, J., Fraser, C., Gutmann, M., Arnold, B., Hanage, W., Bentley, S., Lipsitch, M., and Croucher, N. (2017) · 1960
Earlier work this paper cites.
Survey sampling
Kish, L. (1965) · 1965
Earlier work this paper cites.
Bayesian Statistics
Lindley, D. (1972) · 1972
Earlier work this paper cites.
“Multidimensional Binary Search Trees Used for Associative Searching.”
Bentley, J. L. (1975) · 1975
Earlier work this paper cites.
The application of Bayesian methods for seeking the extremum
Mockus, J., Tiesis, V., and Zilinskas, A. (1978) · 1978
Earlier work this paper cites.
“Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician.”
Rubin, D. B. (1984) · 1984
Earlier work this paper cites.
“Mathematical modeling of corneal epithelial wound healing.”
Dale, P. D., Maini, P. K., and Sherratt, J. A. (1994) · 1994
Earlier work this paper cites.
“Simulation-Based Optimal Design.”
Müller, P. (1999) · 1999
Earlier work this paper cites.
“Population growth of human Y chromosomes: a study of Y chromosome microsatellites.”
Pritchard, J. K., Seielstad, M. T., Perez-Lezaun, A., and Feldman, M. W. (1999) · 1999
Earlier work this paper cites.
“GEANT4-a simulation toolkit.”
Agostinelli, S., Allison, J., Amako, K., Apostolakis, J., M Araujo, H., Arce, P., Asai, M., A Axen, D., Banerjee, S., Barrand, G., Behner, F., Bellagamba, L., Boudreau, J., Broglia, L., Brunengo, A., Chauvie, S., Chuma, J., Chytracek, R., Cooperman, G., and Zschiesche, D. (2003) · 2003
Earlier work this paper cites.
“Bayesian Filtering: From Kalman Filters to Particle Filters, and Beyond.”
Chen, Z. (2003) · 2003
Earlier work this paper cites.
“Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion.”
Swanson, K. R., Bridge, C., Murray, J., and Alvord, E. C. (2003) · 2003
Earlier work this paper cites.
Mathematical Epidemiology
Allen, L. J. S. (2008) · 2008
Earlier work this paper cites.
“Optimal Observation Times in Experimental Epidemic Processes.”
Cook, A. R., Gibson, G. J., and Gilligan, C. A. (2008) · 2008
Earlier work this paper cites.
“A brief introduction to PYTHIA 8.1.”
Sjöstrand, T., Mrenna, S., and Skands, P. (2008) · 2008
Earlier work this paper cites.
“A Tutorial on Particle Filtering and Smoothing: Fifteen Years Later.”
Doucet, A. and Johansen, A. (2009) · 2009
Cited alongside, same era.
“Non-linear regression models for Approximate Bayesian Computation.”
Blum, M. and Francois, O. (2010) · 2010
Cited alongside, same era.
“Statistical inference for noisy nonlinear ecological dynamic systems.”
Wood, S. N. (2010) · 2010
Cited alongside, same era.
“Likelihood-Free Inference in Cosmology: Potential for the Estimation of Luminosity Functions.”
M. Schafer, C. and Freeman, P. (2012) · 2012
Cited alongside, same era.
“Deep Gaussian Processes.”
Damianou, A. and Lawrence, N. (2013) · 2013
Cited alongside, same era.
“Estimating the Transmission Dynamics of Streptococcus pneumoniae from Strain Prevalence Data.”
Numminen, E., Cheng, L., Gyllenberg, M., and Corander, J. (2013) · 2013
“Fundamentals and Recent Developments in Approximate Bayesian Computation.”
Lintusaari, J., Gutmann, M., Dutta, R., Kaski, S., and Corander, J. (2017) · 2017
Later among the works it cites.
“Flexible Statistical Inference for Mechanistic Models of Neural Dynamics.”
Lueckmann, J.-M., Gonçalves, P. J., Bassetto, G., Öcal, K., Nonnenmacher, M., and Macke, J. H. (2017) · 2017
Later among the works it cites.
“Bayesian Design of Experiments Using Approximate Coordinate Exchange.”
Overstall, A. M. and Woods, D. C. (2017) · 2017
Later among the works it cites.
“Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology.”
Alsing, J., Wandelt, B., and Feeney, S. (2018) · 2018
Later among the works it cites.
“Weak Epistasis May Drive Adaptation in Recombining Bacteria.”
Arnold, B., Gutmann, M., Grad, Y., Sheppard, S., Corander, J., Lipsitch, M., and Hanage, W. (2018) · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
“Accelerating ABC methods using Gaussian processes.”
Wilkinson, R. (2014) · 2014
Cited alongside, same era.
“Bayesian indirect inference using a parametric auxiliary model.”
Drovandi, C. C., Pettitt, A. N., and Lee, A. (2015) · 2015
Cited alongside, same era.
“Recombination produces coherent bacterial species clusters in both core and accessory genomes.”
Marttinen, P., Croucher, N., Gutmann, M., Corander, J., and Hanage, W. (2015) · 2015
Cited alongside, same era.
“Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation.”
Vo, B. N., Drovandi, C. C., Pettitt, A. N., and Simpson, M. J. (2015) · 2015
Cited alongside, same era.
“Bayesian optimization for likelihood-free inference of simulator-based statistical models.”
Gutmann, M. and Corander, J. (2016) · 2016
Cited alongside, same era.
“Likelihood-free extensions for Bayesian sequentially designed experiments.”
Hainy, M., Drovandi, C. C., and McGree, J. (2016) · 2016
Cited alongside, same era.
Dinev, T. and Gutmann, M. U. (2018) · 2018
Later among the works it cites.
“Likelihood-free inference via classification.”
Gutmann, M., Dutta, R., Kaski, S., and Corander, J. (2018) · 2018
Later among the works it cites.
Handbook of Approximate Bayesian Computation
Sisson, S., Fan, Y., and Beaumont, M. (2018) · 2018
Later among the works it cites.
“Adaptive Gaussian Copula ABC.”
Chen, Y. and Gutmann, M. U. (2019) · 2019
Later among the works it cites.
“Automatic Posterior Transformation for Likelihood-Free Inference.”
Greenberg, D., Nonnenmacher, M., and Macke, J. (2019) · 2019
Later among the works it cites.
“Efficient acquisition rules for model-based approximate Bayesian computation.”
Järvenpää, M., Gutmann, M., Vehtari, A., and Marttinen, P. (2019) · 2019
Later among the works it cites.
“Efficient Bayesian Experimental Design for Implicit Models.”
Kleinegesse, S. and Gutmann, M. U. (2019) · 2019
Later among the works it cites.
“Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows.”
Papamakarios, G., Sterratt, D., and Murray, I. (2019) · 2019
Later among the works it cites.
“Robust Optimisation Monte Carlo.”
Ikonomov, B. and Gutmann, M. (2020) · 2020
Closest in time.
“Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations.”
Järvenpää, M., Gutmann, M. U., Vehtari, A., and Marttinen, P. (2020) · 2020
Closest in time.
“Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference.”
Meeds, T. and Welling, M. (2015) · 2088
Closest in time.