sequential estimation

Article summary

In statistics, sequential estimation refers to estimation methods in sequential analysis where the sample size is not fixed in advance. Instead, data is evaluated as it is collected, and further sampling is stopped in accordance with a predefined stopping rule as soon as significant results are observed. The generic version is called the optimal Bayesian estimator, which is the theoretical underpinning for every sequential estimator (but cannot be instantiated directly). It includes a Markov process for the state propagation and measurement process for each state, which yields some typical statistical independence relations. The Markov process describes the propagation of a probability distribution over discrete time instances and the measurement is the information one has about each time instant, which is usually less informative than the state. Only the observed sequence will, together with the models, accumulate the information of all measurements and the corresponding Markov process to yield better estimates. From that, the Kalman filter (and its variants), the particle filter, the histogram filter and others can be derived. It depends on the models, which one to use and requires experience to choose the right one. In most cases, the goal is to estimate the state sequence from the measurements.…

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