Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Andrew G. Glen"'
Autor:
Andrew G. Glen, Lawrence M. Leemis
This focuses on the developing field of building probability models with the power of symbolic algebra systems. The book combines the uses of symbolic algebra with probabilistic/stochastic application and highlights the applications in a variety of c
This new edition includes the latest advances and developments in computational probability involving A Probability Programming Language (APPL). The book examines and presents, in a systematic manner, computational probability methods that encompass
Publikováno v:
International Series in Operations Research & Management Science ISBN: 9783319433219
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c9c129c0d05015296ab3478c4d1fbd14
https://doi.org/10.1007/978-3-319-43323-3
https://doi.org/10.1007/978-3-319-43323-3
Publikováno v:
The American Statistician. 68:174-182
We present a fully enumerated bootstrap method to find the empirical system lifetime distribution for a coherent system modeled by a reliability block diagram. Given failure data for individual components of a coherent system, the bootstrap empirical
Publikováno v:
International Series in Operations Research & Management Science ISBN: 9783319433219
This chapter extends the work in the previous chapter in order to automate the bivariate change-of-variables technique for bivariate continuous random variables with arbitrary distributions. The algorithm from the previous chapter for univariate chan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4216eff1367dac36803bf7b7d6fe9552
https://doi.org/10.1007/978-3-319-43323-3_5
https://doi.org/10.1007/978-3-319-43323-3_5
Publikováno v:
International Series in Operations Research & Management Science ISBN: 9783319433219
This chapter presents a generalized version of the univariate change-of-variable technique for transforming continuous random variables. Extending a theorem from Casella and Berger [16] for many–to–1 transformations, we consider more general univ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::68a013a4bde56aa4e557de2e118d684a
https://doi.org/10.1007/978-3-319-43323-3_4
https://doi.org/10.1007/978-3-319-43323-3_4
Publikováno v:
International Series in Operations Research & Management Science ISBN: 9783319433219
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::721cd252a0d4c9b6aac4c109474ed12f
https://doi.org/10.1007/978-3-319-43323-3_15
https://doi.org/10.1007/978-3-319-43323-3_15
Publikováno v:
International Series in Operations Research & Management Science ISBN: 9783319433219
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::595e7d7f8304817f7acb05527b5e0be3
https://doi.org/10.1007/978-3-319-43323-3_11
https://doi.org/10.1007/978-3-319-43323-3_11
Publikováno v:
International Series in Operations Research & Management Science ISBN: 9783319433219
This chapter presents an algorithm for computing the PDF of order statistics drawn from discrete parent populations, along with an implementation of the algorithm in APPL. Several examples illustrate the utility of this algorithm.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a6a40c1b57f87fc3c95a3fb82a362b42
https://doi.org/10.1007/978-3-319-43323-3_9
https://doi.org/10.1007/978-3-319-43323-3_9
Publikováno v:
International Series in Operations Research & Management Science ISBN: 9783319433219
This chapter and the three that follow it concern continuous random variables. We have chosen to present continuous random variables first because they are defined with a somewhat simpler data structure than that for discrete random variables. The de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::088f373daaf2a33ddea8dc728220180a
https://doi.org/10.1007/978-3-319-43323-3_3
https://doi.org/10.1007/978-3-319-43323-3_3