Editing Stochastic process (section)

=== Applications in Computer Science ===

==== Randomized Algorithms ====
Stochastic processes play a critical role in computer science, particularly in the analysis and development of '''randomized algorithms'''. These algorithms utilize random inputs to simplify problem-solving or enhance performance in complex computational tasks. For instance, Markov chains are widely used in probabilistic algorithms for optimization and sampling tasks, such as those employed in search engines like Google's PageRank.<ref name="Randomized algorithms">{{Cite book |title=Randomized algorithms |date=1995 |publisher=Cambridge University Press |isbn=978-0-511-81407-5 |editor-last=Motwani |editor-first=Rajeev |location=Cambridge New York |editor-last2=Raghavan |editor-first2=Prabhakar}}</ref> These methods balance computational efficiency with accuracy, making them invaluable for handling large datasets. Randomized algorithms are also extensively applied in areas such as cryptography, large-scale simulations, and artificial intelligence, where uncertainty must be managed effectively.<ref name="Randomized algorithms"/>

==== Queuing Theory ====
Another significant application of stochastic processes in computer science is in '''queuing theory''', which models the random arrival and service of tasks in a system.<ref>{{Cite book |last=Shortle |first=John F. |title=Fundamentals of queueing theory |last2=Thompson |first2=James M. |last3=Gross |first3=Donald |last4=Harris |first4=Carl M. |date=2017 |publisher=John Wiley & Sons |isbn=978-1-118-94352-6 |edition=Fifth |series=Wiley series in probability and statistics |location=Hoboken, New Jersey}}</ref> This is particularly relevant in network traffic analysis and server management. For instance, queuing models help predict delays, manage resource allocation, and optimize throughput in web servers and communication networks. The flexibility of stochastic models allows researchers to simulate and improve the performance of high-traffic environments. For example, queueing theory is crucial for designing efficient data centers and cloud computing infrastructures.<ref>{{Cite book |title=Fundamentals of queueing theory |date=2018 |publisher=John Wiley & Sons |isbn=978-1-118-94356-4 |edition=5 |location=Hoboken}}</ref>