Basics of Applied Stochastic Processes

By R. Serfozo. 2012

334 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research

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Citations in the corpus

Batch Stationary Distribution Estimation (2020, methodology): “Following the standard Kendall’s notation in queueing theory (Haviv, 2009; Serfozo, 2009), we analyze the discrete-time Geo/Geo/1 queue, which is commonly used in the literature (Atencia and Moreno, 2004; Li and Tian, 2008; Wang et al.”
An Algorithmic Perspective on Imitation Learning (2018, background): “A sequence of states x0, ..., xt is a Markov chain if at any time t, the future states xt+1, xt+2, ... depend on the history x0, ..., xt only through the present state xt [Serfozo, 2009].”
Optimal Selfish Mining Strategies in Bitcoin (2015, methodology): “We can thus use The Strong Law of Large Numbers for Markov chains (see, e.g., [16] pg. 50, Corollary 79) to arrive at (14) and (16).”
Translation-based Recommendation (2017, background): “Likewise, (ﬁrst-order) Markov Chain (MC) models [Serfozo, 2009] capture transition relationships between pairs of adjacent items in sequences (i.e., sequential dynamics), often by way of factor-izing the transition matrix in favor of generalization ability.”
Secure High-Rate Transaction Processing in Bitcoin (2015, background): “7 See Theorem 54, Chapter 2 in [17] for the compatibility of these two interpretations of β.”
Stochastic modeling of twin nucleation in polycrystals: An application in hexagonal close-packed metals (2014, methodology): “This type of model is referred to as Cox process or sometimes a doubly-stochastic Poisson process, and techniques for their analysis and simulation are available in the statistics literature (Serfozo, 2009).”