- Author:Dean L. Isaacson
- Publisher:Krieger Pub Co (June 1, 1976)
- Pages:256 pages
- Subcategory:Science & Mathematics
- FB2 format1333 kb
- ePUB format1277 kb
- DJVU format1688 kb
- Formats:lrf rtf lit lrf
folkscanomy; additional collections. Markov Chains Theory and Applications.
folkscanomy; additional collections. ark:/13960/t9d56652p. Ocr. ABBYY FineReader 1.
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Dust jacket notes: "MARKOV CHAINS is a practical book based on proven theory for those who use Markov models in their work. Isaacson/Madsen take up the topic of Markov chains, emphasizing discrete time chains
Dust jacket notes: "MARKOV CHAINS is a practical book based on proven theory for those who use Markov models in their work. Isaacson/Madsen take up the topic of Markov chains, emphasizing discrete time chains. It is rigorous mathematically but not restricted to mathematical aspects of the Markov chain theory. The authors stress the practical aspects of Markov chains through numerous examples and illustrations.
Theory and Applications. Modern Methods, Problems and Applications of Operator Theory and Harmonic Analysis VIII. Publishing year: 1976. Stochastic and Analytic Methods in Mathematical Physics.
Markov chains theory and applications, Dean L. Isaacson and Richard W. Madsen. The application of Markov models as sting tools has been widely documented in the practice of infrastructure management.
A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. In continuous-time, it is known as a Markov process. It is named after the. It is named after the Russian mathematician Andrey Markov.
Markov Chain Stochastic Process Probability Theory Mathematical Biology Nonhomogeneous Markov Chain. Bowerman, . David, H. Isaacson, . Uniform strong ergodicities of Markov chains. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves. Preprint from Iowa State University (1975)Google Scholar. 2. Iosifescu, . On two recent papers on ergodicity in nonhomogeneous Markov chains.
Markov chains are a fundamental class of stochastic processes. The author presents the theory of both discrete-time and continuous-time homogeneous Markov chains.