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A very powerful approach that allows us to extract, in an adaptive manner, information from observed date is that of filtering. The aim of this chapter is to introduce filtering of information about hidden variables that evolve over time. These variables may follow continuous time hidden Markov chains or may satisfy certain hidden SDEs. Their observation is considered to be perturbed by the noise of Wiener or other processes. Approximate discrete-time filters driven by observation processes will be constructed for different purposes.
KeywordsWiener Process Time Step Size Contingent Claim Observation Process Strong Order
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