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Approximate Bayesian computation (ABC) is a powerful and elegant framework for performing inference in simulation-based models.
Stochastic estimation of the maximum of a regression function
Kiefer, Jack, Wolfowitz, Jacob, et al · 1952
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Hybrid monte carlo
Duane, Simon, Kennedy, Anthony D, Pendleton, Brian J, and Roweth, Duncan · 1987
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Multivariate stochastic approximation using a simultaneous perturbation gradient approximation
Spall, James C · 1992
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Simulation-based optimization with stochastic approximation using common random numbers
Kleinman, Nathan L, Spall, James C, and Naiman, Daniel Q · 1999
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Adaptive stochastic approximation by the simultaneous perturbation method
Spall, James C · 2000
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Approximate bayesian computation in population genetics
Beaumont, Mark A, Zhang, Wenyang, and Balding, David J · 2002
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Markov chain monte carlo without likelihoods
Marjoram, Paul, Molitor, John, Plagnol, Vincent, and Tavaré, Simon · 2003
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Gaussian processes to speed up hybrid monte carlo for expensive bayesian integrals
Rasmussen, C.E · 2003
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Monte carlo computation of the fisher information matrix in nonstandard settings
Spall, James C · 2005
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Sequential monte carlo without likelihoods
Sisson, SA, Fan, Y, and Tanaka, Mark M · 2007
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The pseudo-marginal approach for efficient monte carlo computations
Andrieu, C. and Roberts, G · 2009
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A metropolis adjusted nosé-hoover thermostat
Leimkuhler, Benedict and Reich, Sebastian · 2009
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Likelihood-free markov chain monte carlo
Sisson, Scott A and Fan, Yanan · 2010
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Statistical inference for noisy nonlinear ecological dynamic systems
Wood, Simon N · 2010
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Mcmc using hamiltonian dynamics
Neal, Radford M · 2011
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How to view an mcmc simulation as a permutation, with applications to parallel simulation and improved importance sampling
Neal, Radford M · 2012
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Gradient free parameter estimation for hidden markov models with intractable likelihoods
Ehrlich, Elena, Jasra, Ajay, and Kantas, Nikolas · 2013
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Approximate bayesian computation via regression density estimation
Fan, Yanan, Nott, David J, and Sisson, Scott A · 2013
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Approximate bayesian computation (ABC) gives exact results under the assumption of model error
Wilkinson, R · 2013
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Stochastic gradient hamiltonian monte carlo
Chen, Tianqi, Fox, Emily B, and Guestrin, Carlos · 2014
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Bayesian sampling using stochastic gradient thermostats
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Bayesian learning via stochastic gradient langevin dynamics
Welling, Max and Teh, Yee W · 2011
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Approximate bayesian computational methods
Marin, J.-M., Pudlo, P., Robert, C.P., and Ryder, R.J · 2012
Cited alongside, same era.
Driving markov chain monte carlo with a dependent random stream
Murray, Iain and Elliott, Lloyd T · 2012
Cited alongside, same era.
Ding, Nan, Fang, Youhan, Babbush, Ryan, Chen, Changyou, Skeel, Robert D, and Neven, Hartmut · 2014
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GPS-ABC: Gaussian process surrogate approximate bayesian computation
Meeds, Edward and Welling, Max · 2014
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A generalized, likelihood-free method for posterior estimation
Turner, Brandon M. and Sederberg, Per B · 2014
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Accelerating abc methods using gaussian processes
Wilkinson, R · 2014
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