Markov-process Approximation
Markov-Process Approximation is a stochastic modeling technique that belongs to the class of heavy-traffic approximations, specifically characterized by the use of a continuous-time Markov process to approximate the dynamics of a queueing system under high load conditions.
861
Publications
46.8K
Citations
1.8K
Authors
732
Institutions
Publications per year
2017–2026
148
Parents
861
Three models for the description of language
Noam Chomsky · IEEE Transactions on Information Theory · 1956 · 2.8K citations
1–5 of 861
1.8K
7
h-index
7
h-index
6
h-index
6
h-index
6
h-index
1–5 of 1.8K
732
Columbia University
New York, United States
16 h-index
25 publications
2.7K citations
Stanford University
Stanford, United States
15 h-index
31 publications
3K citations
University of Cambridge
Cambridge, United Kingdom
13 h-index
18 publications
1.1K citations

Centre National de la Recherche Scientifique
Paris, France
13 h-index
27 publications
883 citations
Princeton University
Princeton, United States
12 h-index
18 publications
1.8K citations
Massachusetts Institute of Technology
Cambridge, United States
11 h-index
16 publications
3.7K citations
AT&T (United States)
Bedminster, United States
11 h-index
14 publications
3.3K citations

University of Toronto
Toronto, Canada
11 h-index
19 publications
838 citations
1–8 of 732
Repository · 26 publications · 957 citations
18
h-index
22 publications · 1.2K citations
17
h-index
16 publications · 1.7K citations
15
h-index
13 publications · 570 citations
13
h-index
14 publications · 955 citations
12
h-index
1–5