Bayesian signal analysis (BSA) is a method designed to be optimal for analyzing a relatively short time series, which method can work with a Signal-to-Noise Ratio (SNR) as low as 0.6. No apriori hypotheses are made about the actual series belonging to any hypothetical ensemble or infinite series; only the given data are used to find the probability of some a priori signal being contained in the data. A measure of the analysis accuracy of the estimate can also be obtained.

This article is written at a definitional level only. Authors wishing to expand this entry are inivited to expand the present treatment, which additions will be peer reviewed prior to publication of any expansion.

Further Reading

Friedman, N.; Linial, M.; Nachman, I.; Pe'er, D. (2000). "Using Bayesian Networks to Analyze Expression Data". Journal of Computational Biology7 (3–4): 601–620.

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