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Markov chain Monte Carlo is a widely-used technique for generating a dependent sequence of samples from complex distributions.
Pseudorandom numbers for modelling Markov chains
Chentsov, N. N. (1967) · 1967
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Monte Carlo sampling methods using Markov chains and their applications
Hastings, W. K. (1970) · 1970
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Non-uniform random variate generation
Devroye, L. (1986) · 1986
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Performance of a stochastic learning microchip
Alspector, J., B. Gupta, and R. B. Allen (1989) · 1989
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Derivative-free adaptive rejection sampling for Gibbs sampling
Gilks, W. R. (1992) · 1992
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Adaptive rejection sampling for Gibbs sampling
Gilks, W. R. and P. Wild (1992) · 1992
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Probabilistic inference using Markov chain Monte Carlo methods
Neal, R. M. (1993) · 1993
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Markov chains for exploring posterior distributions
Tierney, L. (1994) · 1994
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Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
Green, P. J. (1995) · 1995
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Bad subsequences of well-known linear congruential pseudorandom number generators
Entacher, K. (1998) · 1998
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The Intel random number generator
Jun, B. and P. Kocher (1999) · 1999
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Pitfalls in computing with pseudorandom determinants
Gärtner, B. (2000) · 2000
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Markov chain sampling methods for Dirichlet process mixture models
Neal, R. (2000) · 2000
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Numerical recipes in C
Press, W. H., S. A. Teukolsky, W. T. Vetterlin, and B. P. Flannery (2002) · 2002
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Recent developments in explicit constructions of extractors
Shaltiel, R. (2002) · 2002
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Slice sampling
Neal, R. M. (2003) · 2003
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Software for flexible Bayesian modeling and Markov chain sampling (FBM)
Analysis of the Linux random number generator
Gutterman, Z., B. Pinkas, and T. Reinman (2006) · 2006
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CODA: Convergence diagnosis and output analysis for MCMC
Plummer, M., N. Best, K. Cowles, and K. Vines (2006) · 2006
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A strong nonrandom pattern in Matlab default random number generator
Savicky, P. (2006) · 2006
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Church: a language for generative models
Goodman, N. D., V. K. Mansinghka, D. M. Roy, K. Bonawitz, and J. B. Tenenbaum (2008) · 2008
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The BUGS project: evolution, critique and future directions
Lunn, D., D. Spiegelhalter, A. Thomas, and N. Best (2009) · 2009
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Theory acquisition as stochastic search
Ullman, T. D., N. D. Goodman, and J. B. Tenenbaum (2010) · 2010
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BayeSys and MassInf
Skilling, J. (2004) · 2004
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A quasi-Monte Carlo Metropolis algorithm
Owen, A. and S. Tribble (2005) · 2005
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Neural dynamics as sampling: A model for stochastic computation in recurrent networks of spiking neurons
Buesing, L., J. Bill, B. Nessler, and W. Maass (2011) · 2011
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Riemann manifold Langevin and Hamiltonian Monte Carlo methods
Girolami, M. and B. Calderhead (2011) · 2011
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MCMC using Hamiltonian dynamics
Neal, R. M. (2011) · 2011
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