Research Letters in the Information and Mathematical Sciences

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/4332

Research Letters welcomes papers from staff and graduate students at Massey University in the areas of: Computer Science, Information Science, Mathematics, Statistics and the Physical and Engineering Sciences. Research letters is a preprint series that accepts articles of completed research work, technical reports, or preliminary results from ongoing research. After editing, articles are published online and can be referenced, or handed out at conferences. Copyright remains with the authors and the articles can be used as preprints to academic journal publications or handed out at conferences. Editors Dr Elena Calude Dr Napoleon Reyes The guidelines for writing a manuscript can be accessed here.

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Author: search.filters.author.Mills, B.I.

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    A model of rainfall based on finite-state cellular automata
    (Massey University, 2004) Munroe, D.R.; Mills, B.I.; Cowpertwait, P.S.P.
    The purpose of this paper is to demonstrate that a finite state cellular automata model is suitable for modeling rainfall in the space-time plane. The time-series properties of the simulated series are matched with historical rainfall data gathered from Whenuapai, NZ. The spatial scale of the model cells in related to land-area by optimizing the cross-correlation between sites at lag 0 relative to rainfall data collected from Auckland, NZ. The model is shown to be adequate for simulation in time, but inadequate in spatial dimension for short distances.
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    Entropies defined by parsed algorithms
    (Massey University, 2003) Mills, B.I.
    Common deterministic measures of the information content of symbolic strings revolve around the resources used in describing or parsing the string. The well known and successful Lempel-Ziv parsing process is described briefly, and compared to the lessor known Titchener parsing process that might have certain theoretical advantages in the study of the nature of deterministic information in strings. Common to the two methods we find that the maximal complexity is asymptotic to hn/ log n, where h is a probabilistic entropy and n is the length of the string. By considering a generic parsing process that can be used to define string complexity, it is shown that this complexity bound appears as a consequence of the counting of unique words, rather than being a result specific to any particular parsing process.