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RESEARCH PRODUCT

Population Monte Carlo Schemes with Reduced Path Degeneracy

Monica F. BugalloDavid LuengoLuca MartinoVictor Elvira

subject

Computational complexity theoryMonte Carlo methodApproximation algorithm020206 networking & telecommunications02 engineering and technology01 natural sciencesStatistics::ComputationWeighting010104 statistics & probabilitysymbols.namesake[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingGaussian noiseResamplingPath (graph theory)0202 electrical engineering electronic engineering information engineeringsymbols0101 mathematicsDegeneracy (mathematics)Algorithm[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingComputingMilieux_MISCELLANEOUS

description

Population Monte Carlo (PMC) algorithms are versatile adaptive tools for approximating moments of complicated distributions. A common problem of PMC algorithms is the so-called path degeneracy; the diversity in the adaptation is endangered due to the resampling step. In this paper we focus on novel population Monte Carlo schemes that present enhanced diversity, compared to the standard approach, while keeping the same implementation structure (sample generation, weighting and resampling). The new schemes combine different weighting and resampling strategies to reduce the path degeneracy and achieve a higher performance at the cost of additional low computational complexity cost. Computer simulations compare the different alternatives in a frequency estimation problem with superimposed sinusoids embedded in Gaussian noise.

https://hal.science/hal-01684860