Particle Filter
A Bayesian filtering method that approximates a continuous probability distribution using a finite set of samples called particles. Commonly applied in POMDPs for belief-state estimation, each particle represents a hypothesis about the true underlying state of the environment.
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Particle filters can theoretically handle arbitrary distributions, but suffer from particle degeneracy in high-dimensional spaces, causing computational costs to increase sharply.
Particle filtering approximates a continuous probability distribution using a finite set of samples (particles) to represent the belief state.
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Particle filters can theoretically handle arbitrary distributions, but suffer from particle degeneracy in high-dimensional spaces, causing computational costs to increase sharply.
50%UnverifiedParticle filtering approximates a continuous probability distribution using a finite set of samples (particles) to represent the belief state.
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