
Particle filter with rejection control and unbiased estimator of the marginal likelihood
We consider the combined use of resampling and partial rejection control...
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An Adaptive ResampleMove Algorithm for Estimating Normalizing Constants
The estimation of normalizing constants is a fundamental step in probabi...
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kmeans: Fighting against Degeneracy in Sequential Monte Carlo with an Application to Tracking
For regular particle filter algorithm or Sequential Monte Carlo (SMC) me...
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A defensive marginal particle filtering method for data assimilation
The Particle filtering (PF) method is often used to estimate the states ...
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Dual Sequential Monte Carlo: Tunneling Filtering and Planning in Continuous POMDPs
We present the DualSMC network that solves continuous POMDPs by learning...
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Asynchronous Anytime Sequential Monte Carlo
We introduce a new sequential Monte Carlo algorithm we call the particle...
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A practical example for the nonlinear Bayesian filtering of model parameters
In this tutorial we consider the nonlinear Bayesian filtering of static...
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Toward Practical N2 Monte Carlo: the Marginal Particle Filter
Sequential Monte Carlo techniques are useful for state estimation in nonlinear, nonGaussian dynamic models. These methods allow us to approximate the joint posterior distribution using sequential importance sampling. In this framework, the dimension of the target distribution grows with each time step, thus it is necessary to introduce some resampling steps to ensure that the estimates provided by the algorithm have a reasonable variance. In many applications, we are only interested in the marginal filtering distribution which is defined on a space of fixed dimension. We present a Sequential Monte Carlo algorithm called the Marginal Particle Filter which operates directly on the marginal distribution, hence avoiding having to perform importance sampling on a space of growing dimension. Using this idea, we also derive an improved version of the auxiliary particle filter. We show theoretic and empirical results which demonstrate a reduction in variance over conventional particle filtering, and present techniques for reducing the cost of the marginal particle filter with N particles from O(N2) to O(N logN).
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