Inference of Markov Chain Models by Using k-Testable Language: Application on Aging People

摘要

We investigate the contribution of unsupervised learning and regular grammatical inference to respectively identify profiles of elderly people and their development over time in order to evaluate care needs (human, financial and physical resources). Grammatical Inference (also known as automata induction, grammar induction and automatic language acquisition) allows grammar and language learning from data. Machine learning by using grammar has a variety of applications: pattern recognition, adaptive intelligent agents, diagnosis, biology, systems modelling, prediction, natural language acquisition, data mining… The proposed approach is based on regular grammar. An adaptation of k-Testable Languages in the Strict Sense Inference algorithm is proposed in order to infer a probabilistic automaton from which a Markovian model which has a discrete (finite or countable) state-space has been deduced. In simulating the corresponding Markov chain model, it is possible to obtain information on population ageing. We have verified if our observed system conforms to a unique long term state vector, called the stationary distribution and the steady-state.