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Parametric statistical models that are implicitly defined in terms of a stochastic data generating process are used in a wide range of scientific disciplines because they enable accurate modeling.
Stock and recruitment
W. E. Ricker · 1954
Earlier work this paper cites.
Exact stochastic simulation of coupled chemical reactions
D. T. Gillespie · 1977
Earlier work this paper cites.
Monte carlo methods of inference for implicit statistical models
P. J. Diggle and R. J. Gratton · 1984
Earlier work this paper cites.
Simulation-Based Econometric Methods (Core Lectures)
C. Gouriéroux and A. Monfort · 1996
Earlier work this paper cites.
Inferring coalescence times from dna sequence data
S. Tavaré, D. J. Balding, R. C. Griffiths, and P. Donnelly · 1997
Earlier work this paper cites.
The vanishing gradient problem during learning recurrent neural nets and problem solutions
S. Hochreiter · 1998
Earlier work this paper cites.
Population growth of human y chromosomes: a study of y chromosome microsatellites
J. K. Pritchard, M. T. Seielstad, A. Perez-Lezaun, and M. W. Feldman · 1999
Earlier work this paper cites.
Effects of stochastic parametrizations in the lorenz ’96 system
D. S. Wilks · 2005
Earlier work this paper cites.
Bayesian inference for a discretely observed stochastic kinetic model
R. J. Boys, D. J. Wilkinson, and T. B. L. Kirkwood · 2008
Earlier work this paper cites.
Approximate bayesian computation scheme for parameter inference and model selection in dynamical systems
T. Toni, D. Welch, N. Strelkowa, A. Ipsen, and M. P. Stumpf · 2009
Earlier work this paper cites.
Non-linear regression models for Approximate Bayesian Computation
M. Blum and O. Francois · 2010
Earlier work this paper cites.
Regularization paths for generalized linear models via coordinate descent
J. Friedman, T. Hastie, and R. Tibshirani · 2010
Earlier work this paper cites.
Statistical inference for noisy nonlinear ecological dynamic systems
S. N. Wood · 2010
Earlier work this paper cites.
Bregman divergence as general framework to estimate unnormalized statistical models
M. Gutmann and J. Hirayama · 2011
Earlier work this paper cites.
Statistical inference for stochastic simulation models – theory and application
F. Hartig, J. M. Calabrese, B. Reineking, T. Wiegand, and A. Huth · 2011
Earlier work this paper cites.
A novel approach for choosing summary statistics in approximate Bayesian computation
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