Skew-Laplace approximation improves posterior density recovery for Dirichlet process mixtures by about 30 percent over standard Laplace and runs substantially faster than MCMC.
Katsevich,The Laplace approximation accuracy in high dimensions: a refined analysis and new skew adjustment, 2024, arXiv:2306.07262 [math.ST]
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Applies Bayesian design optimization to the marginalized posterior for non-linear parameter estimation under white noise, with examples of exponentially decaying signals.
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Laplace and skew-Laplace approximations for Dirichlet process mixture posterior density
Skew-Laplace approximation improves posterior density recovery for Dirichlet process mixtures by about 30 percent over standard Laplace and runs substantially faster than MCMC.
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Optimal and Adaptive Bayesian Sampling for Non-Linear Parameter Estimation under White Noise
Applies Bayesian design optimization to the marginalized posterior for non-linear parameter estimation under white noise, with examples of exponentially decaying signals.