| Autor: |
Mendes, Fábio Macêdo, Figueiredo, Anníbal Dias |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 12/8/2009, Vol. 1193 Issue 1, p227-234, 8p |
| Abstrakt: |
The moving average smoother decomposes time-series data x(t) into a systematic part plus fluctuations, i.e., x(t) = x(t)+δx(t). In the language of Bayesian inference, smoothing can be understood as the inverse problem of finding the systematic component x(t). from the noisy time-series data x(t) This can be accomplished by a straightforward Bayesian analysis after assigning a prior probability to the functions x(t) and δx(t). We use Gaussian probabilities and approximate the calculations using a free field theory. This contribution generalizes a previous work in order to deal with multidimensional time-series. The full solution is obtained: the posterior, the predictive probability and the evidence. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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