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Approximate Bayesian Computation (ABC) methods are used to approximate posterior distributions in models with unknown or computationally intractable likelihoods.
Kolmogorov, A. N. (1942) Definition of center of dispersion and measure of accuracy from a finite number of observations. Izv. Akad. Nauk S.S.S.R. Ser. Mat
1942
Earlier work this paper cites.
Onsager, L. (1944). Crystal statistics. I. A two-dimensional model with an order-disorder transition. Physical Review
1944
Earlier work this paper cites.
Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research
1958
Earlier work this paper cites.
Landau, D. P. (1976). Finite-size behavior of the Ising square lattice. Physical Review B
1976
Earlier work this paper cites.
Nowlan, S. J., and Hinton, G. E. (1992). Simplifying neural networks by soft weight-sharing. Neural computation
1992
Earlier work this paper cites.
Faragó, A., and Lugosi, G. (1993). Strong universal consistency of neural network classifiers. Information Theory, IEEE Transactions on
1993
Earlier work this paper cites.
Tavaré, S., Balding, D. J., Griffiths, R. C. and Donnelly, P. (1997). Inferring coalescence times from DNA sequence data. Genetics
1997
Earlier work this paper cites.
Fu, Y. X. and Li, W. H. (1997). Estimating the age of the common ancestor of a sample of DNA sequences. Molecular biology and evolution
1997
Earlier work this paper cites.
Weiss, G. and von Haeseler, A. (1998). Inference of population history using a likelihood approach. Genetics
1998
Earlier work this paper cites.
Lehmann, E. L. and Casella, G. (1998). Theory of point estimation
1998
Earlier work this paper cites.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE
1998
Earlier work this paper cites.
Pritchard, J. K., Seielstad, M. T., Perez-Lezaun, A., and Feldman, M. W. (1999). Population growth of human Y chromosomes: a study of Y chromosome microsatellites. Molecular biology and evolution
1999
Earlier work this paper cites.
Caruana, R., Lawrence, S. and Giles, C.L. (2000). Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping. In Advances in Neural Information Processing Systems 13: Proceedings of the 2000 Conference
2000
Cited alongside, same era.
Marjoram, P., Molitor, J., Plagnol, V., and Tavaré, S. (2003). Markov chain Monte Carlo without likelihoods. Proceedings of the National Academy of Sciences
2003
Cited alongside, same era.
Ng, A. Y. (2004, July). Feature selection, L1 vs. L2 regularization, and rotational invariance. In Proceedings of the twenty-first international conference on Machine learning
2004
Cited alongside, same era.
Hinton, G. E. and Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science
2006
Cited alongside, same era.
Hinton, G. E., Osindero, S., and Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation
Beaumont, M. A. (2010). Approximate Bayesian computation in evolution and ecology. Annual review of ecology, evolution, and systematics
2010
Later among the works it cites.
Csilléry, K., Blum, M. G., Gaggiotti, O. E. and François, O. (2010). Approximate Bayesian computation (ABC) in practice. Trends in ecology & evolution
2010
Later among the works it cites.
Nunes, M. A. and Balding, D. J. (2010). On optimal selection of summary statistics for approximate Bayesian computation. Statistical applications in genetics and molecular biology
2010
Later among the works it cites.
Blum, M. G. and François, O. (2010). Non-linear regression models for Approximate Bayesian Computation. Statistics and Computing
2010
Later among the works it cites.
Le Roux, N., and Bengio, Y. (2010). Deep belief networks are compact universal approximators. Neural computation
2010
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2006
Cited alongside, same era.
Asmussen, S., and Glynn, P. W. (2007). Stochastic simulation: Algorithms and analysis
2007
Cited alongside, same era.
Sisson, S. A., Fan, Y. and Tanaka, M. M. (2007). Sequential monte carlo without likelihoods. Proceedings of the National Academy of Sciences
2007
Cited alongside, same era.
Joyce, P. and Marjoram, P. (2008). Approximately sufficient statistics and Bayesian computation. Statistical applications in genetics and molecular biology
2008
Cited alongside, same era.
Sutskever, I., and Hinton, G. E. (2008). Deep, narrow sigmoid belief networks are universal approximators. Neural Computation
2008
Cited alongside, same era.
Toni, T., Welch, D., Strelkowa, N., Ipsen, A. and Stumpf, M. P. (2009). Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. Journal of the Royal Society Interface
2009
Cited alongside, same era.
Wegmann, D., Leuenberger, C. and Excoffier, L. (2009). Efficient approximate Bayesian computation coupled with Markov chain Monte Carlo without likelihood. Genetics
2009
Cited alongside, same era.
Lopes, J. S. and Beaumont, M. A. (2010). ABC: a useful Bayesian tool for the analysis of population data. Infection, Genetics and Evolution
2010
Cited alongside, same era.
Later among the works it cites.
Marin, J. M., Pudlo, P., Robert, C. P. and Ryder, R. J. (2012). Approximate Bayesian computational methods. Statistics and Computing
2012
Later among the works it cites.
Fearnhead, P. and Prangle, D. (2012). Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation. Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2012
Later among the works it cites.
Sunnåker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M. and Dessimoz, C. (2013). Approximate Bayesian computation. PLoS Comput. Biol
2013
Later among the works it cites.
Blum, M. G., Nunes, M. A., Prangle, D., Sisson, S. A. (2013). A comparative review of dimension reduction methods in approximate Bayesian computation. Statistical Science
2013
Later among the works it cites.
Bengio, Y., Courville, A., and Vincent, P. (2013). Representation learning: A review and new perspectives. Pattern Analysis and Machine Intelligence, IEEE Transactions on
2013
Later among the works it cites.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks
2015
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Beaumont, M. A., Zhang, W., and Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics
2035
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