出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Markov chain theory isan important tool in applied probability that is quite useful in modelingreal-world computing applications.For a long time, rresearchers have used Markov chains fordata modeling in a wide range of applications that belong to different fields such ascomputational linguists, image processing, communications,bioinformatics, finance systems,etc. This paper explores the Markov chain theory and its extension hidden Markov models(HMM) in natural language processing (NLP) applications. This paper also presents someaspects related to Markov chains and HMM such as creating transition matrices, calculatingdata sequence probabilities, and extracting the hidden states.
