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2015

Ensemble Transport Adaptive Importance Sampling

Cotter, Colin, Cotter, Simon, Russell, Paul

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Markov chain Monte Carlo methods are a powerful and commonly used family of numerical methods for sampling from complex probability distributions.

As applications of these methods increase in size and complexity, the need for efficient methods increases.
In this paper, we present a particle ensemble algorithm.
At each iteration, an importance sampling proposal distribution is formed using an ensemble of particles.

Ensemble Transport Adaptive Importance Sampling · Around

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