Bayesian signal analysis (BSA)

July 2, 2012, 5:08 pm

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 Biology 7 (3–4): 601–620.
  • Physical Oceanography Index


Baum, S. (2012). Bayesian signal analysis (BSA). Retrieved from


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