Fetching the paper…
Reading the bibliography…
Likelihood-free methods are an established approach for performing approximate Bayesian inference for models with intractable likelihood functions.
Bayesian inference using synthetic likelihood: asymptotics and adjustments
Frazier, D. T., Nott, D. J., Drovandi, C., and Kohn, R. (2019) · 1902
Earlier work this paper cites.
Robust approximate bayesian inference with synthetic likelihood
Frazier, D. T. and Drovandi, C. (2019) · 1904
Earlier work this paper cites.
An, Z., South, L. F., and Drovandi, C. (2019b) · 1907
Earlier work this paper cites.
A whitening transformation for two-color blood cell images
Bacus, J. W. (1976) · 1976
Earlier work this paper cites.
Tumour-cell invasion and migration: diversity and escape mechanisms
Friedl, P. and Wolf, K. (2003) · 2003
Earlier work this paper cites.
Spatial point processes and their applications
Baddeley, A., Bárány, I., and Schneider, R. (2007) · 2004
Earlier work this paper cites.
Pattern recognition and machine learning
Bishop, C. M. (2006) · 2006
Earlier work this paper cites.
Penalized normal likelihood and ridge regularization of correlation and covariance matrices
Warton, D. I. (2008) · 2008
Earlier work this paper cites.
The epidemiological fitness cost of drug resistance in Mycobacterium tuberculosis
Luciani, F., Sisson, S. A., Jiang, H., Francis, A. R., and Tanaka, M. M. (2009) · 2009
Earlier work this paper cites.
Wound repair at a glance
Shaw, T. J. and Martin, P. (2009) · 2009
Earlier work this paper cites.
Approximate Bayesian computation: a non-parametric perspective
Blum, M. G. B. (2010) · 2010
Cited alongside, same era.
Statistical inference for noisy nonlinear ecological dynamic systems
Wood, S. N. (2010) · 2010
Cited alongside, same era.
Multivariate statistics: High-dimensional and large-sample approximations
Fujikoshi, Y., Ulyanov, V. V., and Shimizu, R. (2011) · 2011
Cited alongside, same era.
Constructing summary statistics for approximate bayesian computation: semi-automatic approximate bayesian computation
Fearnhead, P. and Prangle, D. (2012) · 2012
Cited alongside, same era.
Approximate Bayesian computation via regression density estimation
Fan, Y., Nott, D. J., and Sisson, S. A. (2013) · 2013
Cited alongside, same era.
Collective cell behaviour with neighbour-dependent proliferation, death and directional bias
Binny, R. N., James, A., and Plank, M. J. (2016) · 2016
Later among the works it cites.
Bayesian optimization for likelihood-free inference of simulator-based statistical models
Gutmann, M. U. and Corander, J. (2016) · 2016
Later among the works it cites.
Bootstrapped synthetic likelihood
Everitt, R. G. (2017) · 2017
Later among the works it cites.
A stochastic movement model reproduces patterns of site fidelity and long-distance dispersal in a population of Fowler’s toads (Anaxyrus fowleri)
Marchand, P., Boenke, M., and Green, D. M. (2017) · 2017
Later among the works it cites.
Inferring parameters for a lattice-free model of cell migration and proliferation using experimental data
Browning, A. P., McCue, S. W., Binny, R. N., Plank, M. J., Shah, E. T., and Simpson, M. J. (2018) · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Lindstrom, T., Brown, G. P., Sisson, S. A., Phillips, B. L., and Shine, R. (2013) · 2013
Cited alongside, same era.
Interpreting scratch assays using pair density dynamics and approximate Bayesian computation
Johnston, S. T., Simpson, M. J., McElwain, D. S., Binder, B. J., and Ross, J. V. (2014) · 2014
Cited alongside, same era.
Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
Doucet, A., Pitt, M., Deligiannidis, M. K., and Kohn, R. (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.
Robust Bayesian synthetic likelihood via a semi-parametric approach
An, Z., Nott, D. J., and Drovandi, C. (2019a)
Cited in the paper.
Accelerating Bayesian synthetic likelihood with the graphical lasso
An, Z., South, L. F., Nott, D. J., and Drovandi, C. C. (2019c)
Cited in the paper.
Likelihood-free inference in high dimensions with synthetic likelihood
Ong, V. M., Nott, D. J., Tran, M.-N., Sisson, S. A., and Drovandi, C. C. (2018a)
Cited in the paper.
Later among the works it cites.
Optimal whitening and decorrelation
Kessy, A., Lewin, A., and Strimmer, K. (2018) · 2018
Later among the works it cites.
High-dimensional approximate Bayesian computation
Nott, D. J., Ong, V. M.-H., Fan, Y., and Sisson, S. A. (2018) · 2018
Later among the works it cites.
Summary statistics
Prangle, D. (2018) · 2018
Later among the works it cites.
Bayesian synthetic likelihood
Price, L. F., Drovandi, C. C., Lee, A., and Nott, D. J. (2018) · 2018
Later among the works it cites.