A. Davis, `Markov Chains as Random Input Automata', in American Mathematical Monthly, Vol.68, pp. 264-267, 1961. Home/Search Document Not in Database Summary Related Articles Check |
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Stochastic Sequential Machines Synthesis with.. - Marculescu.. (1996) (Correct)
....build a SSM which generates an output sequence with given characteristics. The basic procedure involves synthesis of combinational circuits and construction of information sources with prescribed probability distributions. It can be simplified by means of the following important result: Theorem 1 [11]: Any m n stochastic matrix A can be expressed in the form where p i 0, and U i are degenerate stochastic matrices (that is, matrix elements are 0 or 1 only) and the number of matrices U i in the expansion is at most . Ap i U i = p i 1 = mn1 ( 1 7 Proof: p 1 is taken to ....
A. Davis, `Markov Chains as Random Input Automata', in American Mathematical Monthly, Vol.68, pp. 264-267, 1961.
Constrained Sequence Generation Using Stochastic.. - Marculescu.. (1996) (Correct)
....build a SSM which generates an output sequence with given characteristics. The basic procedure involves synthesis of combinational circuits and construction of information sources with prescribed probability distributions. It can be simplified by means of the following important result: Theorem 1 [13]: Any m n stochastic matrix A can be expressed in the form A = p i U i where p i 0, p i = 1, and U i are degenerate stochastic matrices (that is, matrix elements are 0 or 1 only) and the A 0 ( 1 2 1 2 1 4 3 4 = A 1 ( 2 3 1 3 1 2 1 2 = A 00 ( A 0 ( ....
A. Davis, `Markov Chains as Random Input Automata', in American Mathematical Monthly, Vol.68, pp. 264-267, 1961.
Stochastic Sequential Machine Synthesis Targeting.. - Marculescu.. (1996) (5 citations) (Correct)
....machines, that is every Moore type SSM has a Mealy type equivalent and vice versa. B. The synthesis procedure Without loss of generality, in what follows the machines are assumed to be of Moore type. The basic procedure can be simplified by means of the following important result. Theorem 1. [11]: Any m n stochastic matrix A can be expressed in the form A = p i U i where p i 0, p i = 1, and U i are degenerate stochastic matrices (i.e. having only 0 or 1 entries) and the number of matrices U i in the expansion is at most m(n 1) 1. n The theorem we provide next is very ....
A. Davis, `Markov Chains as Random Input Automata', in American Mathematical Monthly, Vol.68, pp. 264-267, 1961.
Adaptive Models for Input Data Compaction for Power.. - Marculescu, Marculescu.. (1997) (3 citations) (Correct)
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A. Davis, `Markov Chains as Random Input Automata', in American Mathematical Monthly, Vol.68, pp. 264-267, 1961.
Vector Compaction Using Dynamic Markov Models - Radu Marculescu (1996) (1 citation) (Correct)
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A. Davis, `Markov Chains as Random Input Automata', in American Mathematical Monthly, Vol.68, pp. 264-267, 1961.
Vector Compaction Using Dynamic Markov Models - Marculescu, Marculescu, Pedram (1996) (1 citation) (Correct)
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A. Davis, `Markov Chains as Random Input Automata', in American Mathematical Monthly, Vol.68, pp. 264-267, 1961.
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