
Pushing the Limits of Importance Sampling through Iterative Moment Matching
The accuracy of an integral approximation via Monte Carlo sampling depen...
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Adaptive importance sampling by kernel smoothing
A key determinant of the success of Monte Carlo simulation is the sampli...
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An Adaptive ResampleMove Algorithm for Estimating Normalizing Constants
The estimation of normalizing constants is a fundamental step in probabi...
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Distributed SMCPHD Fusion for Partial, Arithmetic Average Consensus
We propose an average consensus approach for distributed SMCPHD (sequen...
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Bayesian Fusion of Data Partitioned Particle Estimates
We present a Bayesian data fusion method to approximate a posterior dist...
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Path Throughput Importance Weights
Many Monte Carlo light transport simulations use multiple importance sam...
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Tensor Monte Carlo: particle methods for the GPU era
Multisample objectives improve over singlesample estimates by giving t...
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Approximate Shannon Sampling in Importance Sampling: Nearly Consistent Finite Particle Estimates
In Bayesian inference, we seek to compute information about random variables such as moments or quantiles on the basis of data and prior information. When the distribution of random variables is complicated, Monte Carlo (MC) sampling is usually required. Importance sampling is a standard MC tool for addressing this problem: one generates a collection of samples according to an importance distribution, computes their contribution to an unnormalized density, i.e., the importance weight, and then sums the result followed by normalization. This procedure is asymptotically consistent as the number of MC samples, and hence deltas (particles) that parameterize the density estimate, go to infinity. However, retaining in infnitely many particles is intractable. Thus, we propose a scheme for only keeping a nite representative subset of particles and their augmented importance weights that is nearly consistent.
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