M
Mark Daniel Ward
Researcher at Purdue University
Publications - 54
Citations - 516
Mark Daniel Ward is an academic researcher from Purdue University. The author has contributed to research in topics: Combinatorics on words & Trie. The author has an hindex of 11, co-authored 50 publications receiving 463 citations. Previous affiliations of Mark Daniel Ward include University of Pennsylvania.
Papers
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Data Science in Statistics Curricula: Preparing Students to “Think with Data”
Johanna Hardin,Roger Hoerl,Nicholas J. Horton,Deborah Nolan,Ben Baumer,O. Hall-Holt,Paul Murrell,Roger D. Peng,P. Roback,D. Temple Lang,Mark Daniel Ward +10 more
TL;DR: In this article, the importance of data science proficiency and resources for instructors to implement data science in their own statistics curricula are discussed. But these data science topics have not traditionally been a major component of undergraduate programs in statistics.
Journal ArticleDOI
Asymptotic distribution of two-protected nodes in random binary search trees
TL;DR: The exact moments of the number of 2-protected nodes in binary search trees grown from random permutations are derived using a properly normalized version of this tree parameter.
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Analysis of the average depth in a suffix tree under a Markov model
Julien Fayolle,Mark Daniel Ward +1 more
TL;DR: In this article, it was shown that under a Markovian model of order one, the average depth of suffix trees of index n is asymptotically similar to the average depths of tries (a.k.a. digital trees) built on n independent strings.
Error resilient LZ'77 data compression: Algorithms, analysis, and experiments - eScholarship
TL;DR: A joint source-channel coding algorithm capable of correcting some errors in the popular Lempel-Ziv'77 (LZ'77) scheme without introducing any measurable degradation in the compression performance is proposed.
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Asymptotic properties of protected nodes in random recursive trees
TL;DR: It follows that the number of protected nodes in a random recursive tree, upon proper scaling, converges in probability to a constant.