Filtering with Abstract Particles. Steinhardt, J & Liang, P Proceedings of The 31st International Conference …, 2014.
abstract   bibtex   
Abstract Using particles, beam search and sequential Monte Carlo can approximate distributions in an extremely flexible manner. However, they can suffer from sparsity and inadequate coverage on large state spaces. We present a new filtering method that.
@Article{Steinhardt2014,
author = {Steinhardt, J and Liang, P}, 
title = {Filtering with Abstract Particles}, 
journal = {Proceedings of The 31st International Conference …}, 
volume = {}, 
number = {}, 
pages = {}, 
year = {2014}, 
abstract = {Abstract Using particles, beam search and sequential Monte Carlo can approximate distributions in an extremely flexible manner. However, they can suffer from sparsity and inadequate coverage on large state spaces. We present a new filtering method that.}, 
location = {}, 
keywords = {}}

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