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Skip to Search Results- 2Simulations
- 1Bayesian Model Selection
- 1Branching Process
- 1Central Limit Theorem
- 1Coupling
- 1Exchangeable Random Variables
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2014
Wu, B., Newton, F., Kouritzin, Michael A.
A new class of discrete random fields designed for quick simulation and covariance inference under inhomogenous conditions is introduced and studied. Simulation of these correlated fields can be done in a single pass instead of relying on multi-pass convergent methods like the Gibbs Sampler or...
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2015
Kouritzin, Michael A., Newton, Fraser, Wu, Biao
A real-time algorithm to produce correlated random fields on general undirected graphs has been used in CAPTCHA generation and optical character recognition. This algorithm can not simulate all possible joint graph distributions but does match all marginal vertex distributions as well as...
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2015-04-20
A large class of discrete-time branching particle filters with Bayesian model selection ca-pabilities and effective resampling is introduced in algorithmic form, shown empirically to outperform the popular bootstrap algorithm and analyzed mathematically. The particles interact weakly in the...