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Additionally, using the transition prior as the proposal, which fails to consider the information of current measurements, would easily suffer from the 'particle impoverishment' problem.
In this way, the newest observations are gradually considered to approximate the sampling results from the optimal proposal distribution and hence overcome the sample impoverishment problem suffered by conventional PF.
To further handle sample impoverishment problem suffered by conventional PF, Zhang et al. [9] propose a swarm intelligence-based PF tracking algorithm, where particles are firstly propagated through the state transition model, and then corporately evolved according to particle swarm optimization (PSO) iterations based on the cognitive and social aspects of particle populations.
Table 5 presents the results of logistic regression analysis of the correlates of impoverishment problem.
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In some of those schools money isn't so much a problem as cultural impoverishment – a lack of curiosity from the students.
Here's how gentrification talk typically goes: poor neighborhoods are said to need "regeneration" or "revitalization", as if lifelessness and torpor – as opposed to impoverishment and disempowerment – were the problem.
However, resampling leads to another problem called sampling impoverishment.
The method uses the MCMC to solve the problem of sample impoverishment in UPF algorithm.
Resampling operation solves degeneracy to some extent, but it results in the problem of sample impoverishment.
This PF KS method can simultaneously estimate the degradation state and the unknown parameters in the degradation model, while significantly overcoming the problem of particle impoverishment.
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