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Charles M. Grinstead coauthored Introduction to Probability by Charles M. Grinstead and J. Laurie Snell with J. Laurie Snell. The institutional publication record identifies the1997 second revised edition and its authors.^[ref] The text develops probability from concrete models through tools such as conditional probability, expectation and Markov chains. For example, counting length-$n$ sequences of independent fair coin tosses gives probability$2^{-n}\left(\genfrac{}{}{0ex}{}{n}{k}\right)$ for exactly$k$ heads, connecting discrete counting with a probability model rather than treating either in isolation.

Homepage: works.swarthmore.edu/fac-math-stat/153/

By Charles M. Grinstead and J. Laurie Snell.

Publisher of the underlying book: American Mathematical Society.

Online text: math.dartmouth.edu/~prob/prob/prob.pdf

The linked CHANCE Project version is dated 4 July 2006. Its cover identifies the underlying second-edition book as copyright 2003 by the two authors, and this version as copyright 2006 by Peter G. Doyle. The version credit does not identify Doyle as an original-book coauthor or formal editor. These dates describe copyrights and a version, rather than a claim about first publication.

Cited passages: §3.1, Example 3.3 and Table 3.1, printed pp. 77–78: uniform-calendar assumptions, distinct-label product and threshold.

Section 6.1, Definition 6.1, printed p. 226, defines finite discrete expectation by summing values weighted by their probabilities. This is the discrete calculation used in nonnegative random variable. Online text: math.dartmouth.edu/~prob/prob/prob.pdf .

J. Laurie Snell worked on Markov chains and their applications and wrote Introduction to Probability by Charles M. Grinstead and J. Laurie Snell with Charles M. Grinstead. His autobiographical research statement also describes work on finite mathematics and teaching people to assess probability and statistical claims in the news.^[ref] A Markov chain replaces the full history by a specified present-state transition law; first-step analysis then turns hitting probabilities and expected hitting times into linear equations. That link between a stochastic model and solvable equations is central to the subject's practical usefulness.

Homepage: chance.dartmouth.edu/Laurie.x.html