Semi-Markov Modelling is a stochastic modelling framework that generalizes Markov processes by incorporating arbitrarily distributed state holding times, thereby enabling the explicit representation of temporal dynamics between state transitions in systems with memory.
923
Publications
37.8K
Citations
1.8K
Authors
814
Institutions
Publications per year
2017–2026
185
Variants
Semi-markov Modeling
Parents
Publications
923
Update or Wait: How to Keep Your Data Fresh
Yin Sun, Elif Uysal‐Biyikoglu, Roy D. Yates et al. · IEEE Transactions on Information Theory · 2017 · 843 citations
Incremental Joint Extraction of Entity Mentions and Relations
Opinion Mining with Deep Recurrent Neural Networks
Ozan İrsoy, Claire Cardie · 2014 · 386 citations · Full text
1–5 of 923
Authors
1.8K
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1–5 of 1.8K
Institutions
814
Duke University
Durham, United States
19 h-index
37 publications
2.7K citations
University of Chieti-Pescara
Chieti, Italy
14 h-index
21 publications
426 citations
Purdue University West Lafayette
West Lafayette, United States
13 h-index
28 publications
1.2K citations
Sapienza University of Rome
Rome, Italy
12 h-index
16 publications
505 citations
Stanford University
Stanford, United States
11 h-index
16 publications
1.3K citations
University of Michigan
Ann Arbor, United States
11 h-index
19 publications
1.2K citations
Aristotle University of Thessaloniki
Thessaloniki, Greece
11 h-index
18 publications
644 citations
Université de Technologie de Compiègne
Compiègne, France
10 h-index
17 publications
638 citations
1–8 of 814
Venues
- Journal of Applied Probability
JAP · 44 publications · 1.7K citations
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- IEEE Transactions on Reliability
20 publications · 739 citations
17
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- Advances in Applied Probability
AAP · 20 publications · 863 citations
16
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- Operations Research
18 publications · 915 citations
16
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- Reliability Engineering & System Safety
17 publications · 722 citations
14
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