Population-based Monte Carlo algorithms
نویسنده
چکیده
In this paper, we give a cross-disciplinary survey on “population-based” Monte Carlo algorithms. These algorithms consist of a set of “walkers” or “particles” for the representation of a high-dimensional vector and the computation is carried out by a random walk and split/deletion of these objects. The algorithms are developed in various fields in physics and statistical sciences and called by lots of different terms – “Quantum Monte Carlo”, “Transfer Matrix Monte Carlo”, “Monte Carlo Filter (Particle Filter)”,“Sequential Monte Carlo” and “PERM” etc. Here we discuss them in a coherent framework. We also touch on related algorithms – Genetic Algorithms and Annealed Importance Sampling.
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