An Efficient Filtering Approach to Likelihood Approximation for State-Space Representations
We develop a numerical filtering procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-gaussian state-space models. The procedure approximates necessary integrals using continuous or piecewise-continuous approximations of target densities. Construction is achieved via efficient importance sampling, and approximating densities are adapted to fully incorporate current information.
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