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Approximate Bayesian computation (ABC) is now an established technique for statistical inference used in cases where the likelihood function is computationally expensive or not available.
Exact stochastic simulation of coupled chemical reactions
Gillespie, D. T. (1977) · 1977
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Sequential Imputations and Bayesian Missing Data Problems
Kong, A., Liu, J. S., and Wong, W. H. (1994) · 1994
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A Note On Metropolis-Hastings Kernels For General State Spaces
Tierney, L. (1998) · 1998
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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, dec) · 1999
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The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations
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Markov chain monte carlo without likelihoods
Marjoram, P., Molitor, J., Plagnol, V., and Tavare, S. (2003) · 2003
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Efficient hierarchical MCMC for policy search
Strens, M. (2004) · 2004
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Markov chain Monte Carlo Using an Approximation
Christen, J. A. and Fox, C. (2005, dec) · 2005
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The igraph software package for complex network research
Csardi, G. and Nepusz, T. (2006) · 2006
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Sequential Monte Carlo samplers
Del Moral, P., Doucet, A., and Jasra, A. (2006) · 2006
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An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants
Møller, J., Pettitt, A. N., Reeves, R. W., and Berthelsen, K. K. (2006, jun) · 2006
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MCMC for doubly-intractable distributions
Murray, I., Ghahramani, Z., and MacKay, D. J. C. (2006) · 2006
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Sequential Monte Carlo without likelihoods
Sisson, S. A., Fan, Y., and Tanaka, M. M. (2007, feb) · 2007
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ergm: A package to fit, simulate and diagnose exponential-family models for networks
Hunter, D. R., Handcock, M. S., Butts, C. T., Goodreau, S. M., and Morris, M. (2008) · 2008
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The pseudo-marginal approach for efficient Monte Carlo computations
Andrieu, C. and Roberts, G. O. (2009, apr) · 2009
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Computation of Multivariate Normal and t Probabilities
Genz, A. and Bretz, F. (2009) · 2009
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ABC likelihood-free methods for model choice in Gibbs random fields
Grelaud, A., Robert, C. P., and Marin, J.-M. (2009) · 2009
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Correction: Sequential Monte Carlo without likelihoods
Sisson, S. A., Fan, Y., and Tanaka, M. M. (2009) · 2009
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Bayesian inference for exponential random graph models
Caimo, A. and Friel, N. (2011) · 2011
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Likelihood-free estimation of model evidence
Didelot, X., Everitt, R. G., Johansen, A. M., and Lawson, D. J. (2011) · 2011
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Adaptivity for ABC algorithms: the ABC-PMC scheme
Robert, C. P., Beaumont, M. A., Marin, J.-M., and Cornuet, J.-M. (2011) · 2011
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Stochastic Modelling for Systems Biology
Wilkinson, D. J. (2011) · 2011
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Calibration and evaluation of individual-based models using Approximate Bayesian Computation
van der Vaart, E., Beaumont, M. A., Johnston, A. S. A., and Sibly, R. M. (2015) · 2015
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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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Accelerating inference for diffusions observed with measurement error and large sample sizes using Approximate Bayesian Computation: A case study
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Lazy ABC
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ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2016) · 2016
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Everitt, R. G. (2012) · 2012
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SMC 2 : an efficient algorithm for sequential analysis of state space models
Chopin, N., Jacob, P. E., and Papaspiliopoulos, O. (2013) · 2013
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Evidence and Bayes factor estimation for Gibbs random fields
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Zhou, Y., Johansen, A. M., and Aston, J. A. D. (2016) · 2016
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Bayesian model comparison with un-normalised likelihoods
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Marginal sequential Monte Carlo for doubly intractable models
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A review on statistical inference methods for discrete Markov random fields
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