Abstract
An algorithm is described that computes relative frequencies of occurrence of all arbitrarily long substrings of sequential data, such as are obtained from experiments in learning/memory and verbal interaction. The algorithm offers high speed and provides systematization for the computation of empirical conditional probabilities. Use of this algorithm allows application of probabilistic and information theoretic disciplines to reveal dependencies between events separated arbitrarily in time.
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Anderson, T. W., & Goodman, L. A. Statistical inference about Markov chains. Annals of Mathematical Statistics, 1957, 28, 89–110.
Billingsley, P. Statistical methods in Markov chains. Annals of Mathematical Statistics, 1961, 32, 12–40.
Morris, R. Scatter storage techniques. Communications of the Association for Computing Machinery, 1968, 11, 38–44.
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This research was supported in part by USPHS Grant GM 18058. Computing assistance was obtained from the UCLA Health Sciences Computing Facility, sponsored by NIH Special Research Resources Grant RR-3.
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Nirenberg, L.M., Haber, J. & Moise, S.L. A high-speed algorithm for computing conditional probabilities of substrings of sequentially observed data. Behav. Res. Meth. & Instru. 5, 291–294 (1973). https://doi.org/10.3758/BF03200188
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DOI: https://doi.org/10.3758/BF03200188