Statistical Methods for Absorbing Markov-Chain Models for Learning: Estimation and Identification
- 1 March 1974
- journal article
- Published by Cambridge University Press (CUP) in Psychometrika
- Vol. 39 (1) , 3-22
- https://doi.org/10.1007/bf02291573
Abstract
General algorithms for computing the likelihood of any sequence generated by an absorbing Markov-chain are described. These algorithms enable an investigator to compute maximum likelihood estimates of parameters using unconstrained optimization techniques. The problem of parameter identifiability is reformulated into questions concerning the behavior of the likelihood function in the neighborhood of an extremum. An alternative characterization of the concept of identifiability is proposed. A procedure is developed for deciding whether or not this definition is satisfied.Keywords
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