[1] E. Baum and T. Petrie, Statistical inference for probabilistic
functions of finite state Markov chains, The Annals of Mathematical
Statistics 37(6) (1966).
[2] J. A. Boguslavskiy, A Bayes estimations of nonlinear regression
and adjacent problems, Journal of Computer and Systems Sciences
International 4 (1996), 14-24.
[3] J. A. Boguslavskiy, Polynomial Approximations for Nonlinear
Problems of Estimation and Control, Fizmat, MAIK, 2006.
[4] J. A. Boguslavskiy, A Bayes estimator of parameters of nonlinear
dynamic systems, Mathematical Problems in Engineering, 2009, Article
ID 801475, 21 pages, 2009.
[5] M. Borodovsky and S. Ekisheva, Problems and Solutions in
Biological Sequence Analysis, University Press, Cambridge, 2006.
[6] O. Cappe, E. Moulines and T. Ryden, Interference in Hidden Markov
Models, Springer-Verlag, New York, 2005.
[7] S. E. Levinson, L. R. Rabiner and M. M. Sondhi, An introduction to
the application of the theory of probabilistic function of a Markov
process to automatic speech recognition, Bell System Technical Journal
62 (1983).
[8] R. Rubin, S. R. Eddy, A. Krogh and G. Mitchison, Biological
Sequence Analysis; Probabilistic Models of Proteins and Nucleic Acid,
University Press, Cambridge, 1998.
[9] A. N. Schiryaev, The Probability, 2nd ed., Springer-Verlag, New
York, 1996.
[10] M. Stouna, Applications of the theory of Boolean rings to general
topology, Trans. Amer. Math. Soc. 41 (1937), 375-481.
[11] P. C. Young, Recursive Estimation and Time-Series Analysis,
Springer-Verlag, New York, 1984.