Discrete stochastic variables
Reaction Rate Theory and Rare Events Simulations, Page: 363-401
2017
- 3Citations
- 17Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations3
- Citation Indexes3
- CrossRef3
- Captures17
- Readers17
- 17
Book Chapter Description
A stochastic process is the time evolution of a random variable or a collection of random variables. The range of all possible values is called the state space. Depending on the nature of a random variable, its state space may be continuous or discrete. Examples of discrete random variables in physical chemistry include the configurations of a lattice model, quantized vibrational energy levels, nodes of a network representing configurations of a protein, or integer numbers of species in a biochemical reaction network. Examples of continuous random variables include position along a dihedral angle coordinate or kinetic energies of gas molecules.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/B9780444563491000143; http://dx.doi.org/10.1016/b978-0-44-456349-1.00014-3; http://linkinghub.elsevier.com/retrieve/pii/B9780444563491000143; http://api.elsevier.com/content/article/PII:B9780444563491000143?httpAccept=text/xml; http://api.elsevier.com/content/article/PII:B9780444563491000143?httpAccept=text/plain; https://linkinghub.elsevier.com/retrieve/pii/B9780444563491000143; https://dx.doi.org/10.1016/b978-0-44-456349-1.00014-3
Elsevier BV
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