The Gibbs sampler, Metropolis’ algorithm, and similar iterative simulation methods are related to rejection sampling and importance sampling, two methods which have been traditionally thought of as non-iterative. We explore connections between importance sampling, iterative simulation, and importance-weighted resampling (SIR), and present new algorithms that combine aspects of importance sampli...

Recent studies have shown that power systems protection mechanisms have played a major role in propagating disturbances. Out of the last five major Western Systems Coordinating Council (WSCC) events (the North Ridge earthquake, December 14 1994, July 2 & 3 1996, and August 1

Importance sampling provides a practical, production-proven method for integrating diffuse and glossy surface reflections with arbitrary image-based environment or area lighting constructs. Here, functions are evaluated at random points across a domain to produce an estimate of an integral. When using a large number of sample points, the method produces a very accurate result of the integral an...

We consider lifted importance sampling (LIS), a previously proposed approximate inference algorithm for statistical relational learning (SRL) models. LIS achieves substantial variance reduction over conventional importance sampling by using various lifting rules that take advantage of the symmetry in the relational representation. However, it suffers from two drawbacks. First, it does not take ...

Annealed importance sampling assigns equilibrium weights to a nonequilibrium sample that was generated by a simulated annealing protocol [R. M. Neal, Stat. Comput. 11, 125 (2001)]. The weights may then be used to calculate equilibrium averages, and also serve as an "adiabatic signature" of the chosen cooling schedule. In this paper we demonstrate the method on the 50-atom dileucine peptide and ...

This paper proposes a new adaptive importance sampling (AIS) technique for approximate evaluation of multidimensional integrals. Whereas known AIS algorithms try to find a sampling density that is approximately proportional to the integrand, our algorithm aims directly at the minimization of the variance of the sample average estimate. Our algorithm uses piecewise constant sampling densities, w...

We propose a novel adaptive importance sampling algorithm which incorporates Stein variational gradient decent algorithm (SVGD) with importance sampling (IS). Our algorithm leverages the nonparametric transforms in SVGD to iteratively decrease the KL divergence between importance proposals and target distributions. The advantages of our algorithm are twofold: 1) it turns SVGD into a standard IS...

Uniform sampling of training data has been commonly used in traditional stochastic optimization algorithms such as Proximal Stochastic Gradient Descent (prox-SGD) and Proximal Stochastic Dual Coordinate Ascent (prox-SDCA). Although uniform sampling can guarantee that the sampled stochastic quantity is an unbiased estimate of the corresponding true quantity, the resulting estimator may have a ra...

Importance sampling is often used in machine learning when training and testing data come from different distributions. In this paper we propose a new variant of importance sampling that can reduce the variance of importance samplingbased estimates by orders of magnitude when the supports of the training and testing distributions differ. After motivating and presenting our new importance sampli...

We construct importance sampling schemes for stochastic differential equations with small noise and fast oscillating coefficients. Standard Monte Carlo methods perform poorly for these problems in the small noise limit. With multiscale processes there are additional complications, and indeed the straightforward adaptation of methods for standard small noise diffusions will not produce efficient...