| 摘要 |
A method of performing a sequence of measurements, z, R; M; (t1,t2), of at least one parameter and recursively performing predictions. The method comprising the steps of-based on at least on a first measurement instance (M (tk); (k)), predicting the outcome (x, P) for at least two models (C, S); -after a subsequent measurement instance (M (tk+Tp)(k+Tp)) updating the models (C, S) for the corresponding point in time, whereby the prediction made on the basis of the first measurement instance is updated in the light of the subsequent measurement instance; and-re-arranging at least one model (C, S) for the subsequent measurement instance (tk+Tp) (k+Tp), whereby one updated model influences another updated model. For a model set comprising at least one complementary (C) model and at least one sub (S) model, under the step of rearranging the S model never influences the C model. For a model set comprising exclusively complementary (L, N, R) models, under the step of re-arranging, for a given pair of models within the model set (L, N, R), a model having a higher probability (mu) influences a model having a lesser probability, but wherein a model having a lesser probability (mu) never influences a model having a higher probability.
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