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We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible.
“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.
Applied Numerical Methods
Carnahan, B., Luther, H. A., and Wilkes, J. O. (1969) · 1969
Earlier work this paper cites.
Stochastic simulation
Ripley, B. D. (1987) · 1987
Earlier work this paper cites.
“Estimating normalizing constants and reweighting mixtures.”
Geyer, C. J. (1994) · 1994
Earlier work this paper cites.
“Regression Shrinkage and Selection Via the Lasso.”
Tibshirani, R. (1994) · 1994
Earlier work this paper cites.
“Predictability: a problem partly solved.”
Lorenz, E. (1995) · 1995
Earlier work this paper cites.
“Inferring Coalescence Times From DNA Sequence Data.”
Tavaré, S., Balding, D. J., Griffiths, R. C., and Donnelly, P. (1997) · 1997
Earlier work this paper cites.
“Inferences for case-control and semiparametric two-sample density ratio models.”
Qin, J. (1998) · 1998
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.
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., and Friedman, J. (2001) · 2001
Earlier work this paper cites.
“Population Monte Carlo.”
Cappé, O., Guillin, A., Marin, J.-M., and Robert, C. P. (2004) · 2004
Earlier work this paper cites.
“Semiparametric density estimation under a two-sample density ratio model.”
Cheng, K. and Chu, C. (2004) · 2004
Earlier work this paper cites.
“Effects of stochastic parametrizations in the Lorenz ’96 system.”
Wilks, D. S. (2005) · 2005
Earlier work this paper cites.
“Sequential Monte Carlo samplers.”
Del Moral, P., Doucet, A., and Jasra, A. (2006) · 2006
Earlier work this paper cites.
“Using Approximate Bayesian Computation to Estimate Tuberculosis Transmission Parameters From Genotype Data.”
Tanaka, M. M., Francis, A. R., Luciani, F., and Sisson, S. A. (2006) · 2006
Earlier work this paper cites.
“Discriminative Learning for Differing Training and Test Distributions.”
Bickel, S., Brückner, M., and Scheffer, T. (2007) · 2007
Earlier work this paper cites.
“Unified LASSO Estimation by Least Squares Approximation.”
Wang, H. and Leng, C. (2007) · 2007
Earlier work this paper cites.
“On the “degrees of freedom” of the lasso.”
Zou, H., Hastie, T., and Tibshirani, R. (2007) · 2007
Earlier work this paper cites.
“The pseudo-marginal approach for efficient Monte Carlo computations.”
Andrieu, C. and Roberts, G. O. (2009) · 2009
Earlier work this paper cites.
“Approximate Bayesian Computation in Evolution and Ecology.”
Beaumont, M. A. (2010) · 2010
Earlier work this paper cites.
“Approximate Bayesian Computation: A Nonparametric Perspective.”
Blum, M. G. B. (2010) · 2010
Earlier work this paper cites.
“Regularization Paths for Generalized Linear Models via Coordinate Descent.”
Friedman, J., Hastie, T., and Tibshirani, R. (2010) · 2010
Earlier work this paper cites.
“Bayesian Computation and Model Selection Without Likelihoods.”
Leuenberger, C. and Wegmann, D. (2010) · 2010
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“A family of computationally efficient and simple estimators for unnormalized statistical models.”
Pihlaja, M., Gutmann, M., and Hyvärinen, A. (2010) · 2010
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“Statistical inference for noisy nonlinear ecological dynamic systems.”
Wood, S. N. (2010) · 2010
Cited alongside, same era.
“Bregman divergence as general framework to estimate unnormalized statistical models.”
Gutmann, M. and Hirayama, J. (2011) · 2011
Cited alongside, same era.
“Statistical inference for stochastic simulation models – theory and application.”
Hartig, F., Calabrese, J. M., Reineking, B., Wiegand, T., and Huth, A. (2011) · 2011
Cited alongside, same era.
“A novel approach for choosing summary statistics in approximate Bayesian computation.”
“Learning in Implicit Generative Models.”
Mohamed, S. and Lakshminarayanan, B. (2016) · 2016
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“Fast epsilon-free Inference of Simulation Models with Bayesian Conditional Density Estimation.”
Papamakarios, G. and Murray, I. (2016) · 2016
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“Likelihood-Free Inference by Ratio Estimation.”
Thomas, O., Dutta, R., Corander, J., Kaski, S., and Gutmann, M. (2016) · 2016
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“Fundamentals and Recent Developments in Approximate Bayesian Computation.”
Lintusaari, J., Gutmann, M., Dutta, R., Kaski, S., and Corander, J. (2017) · 2017
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Dutta, R., Bogdan, M., and Ghosh, J. (2012) · 2012
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Fearnhead, P. and Prangle, D. (2012) · 2012
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Hakkarainen, J., Ilin, A., Solonen, A., Laine, M., Haario, H., Tamminen, J., Oja, E., and Järvinen, H. (2012) · 2012
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“Approximate Bayesian computational methods.”
Marin, J.-M., Pudlo, P., Robert, C., and Ryder, R. (2012) · 2012
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Price, L. F., Drovandi, C. C., Lee, A., and Nott, D. J. (2017) · 2017
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Arnold, B., Gutmann, M., Grad, Y., Sheppard, S., Corander, J., Lipsitch, M., and Hanage, W. (2018) · 2018
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Handbook of Approximate Bayesian Computation
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