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Quantile Importance Sampling
Jyotishka Datta, Nicholas G. Polson
In Bayesian inference, the approximation of integrals of the form is a fundamental challenge. Such integrals are cr…
Vertical-likelihood Monte Carlo
Nicholas G. Polson, James G. Scott
In this review, we address the use of Monte Carlo methods for approximating definite integrals of the form , where is a target function (often a likelihoo…
Sampling Polya-Gamma random variates: alternate and approximate techniques
Jesse Windle, Nicholas G. Polson, James G. Scott
Efficiently sampling from the Pólya-Gamma distribution, , is an essential element of Pólya-Gamma data augmentation. Polson et. al (2013) show how to efficiently sample f…
Split Sampling: Expectations, Normalisation and Rare Events
John R. Birge, Changgee Chang, Nicholas G. Polson
In this paper we develop a methodology that we call split sampling methods to estimate high dimensional expectations and rare event probabilities. Split sampling uses an auxiliary…
Particle learning of Gaussian process models for sequential design and optimization
Robert B. Gramacy, Nicholas G. Polson
We develop a simulation-based method for the online updating of Gaussian process regression and classification models. Our method exploits sequential Monte Carlo to produce a fast…