Probability, Random Variables, and Stochastic Processes.
Теория вероятностей, случайные величины и стохастические процессы
1984-12-01
SCID: 54.1/2vh2g7kg
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Markov chainsprobability theoryrandom variablesspectral estimationstochastic processes
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Abstract (AI)
Part 1 Probability and Random Variables 1 The Meaning of Probability 2 The Axioms of Probability 3 Repeated Trials 4 The Concept of a Random Variable 5 Functions of One Random Variable 6 Two Random Variables 7 Sequences of Random Variables 8 Statistics Part 2 Stochastic Processes 9 General Concepts 10 Random Walk and Other Applications 11 Spectral Representation 12 Spectral Estimation 13 Mean Square Estimation 14 Entropy 15 Markov Chains 16 Markov Processes and Queueing Theory
Key Findings
1
Applications include Markov processes and queueing theory, connecting stochastic-process methods to applied probabilistic systems.
2
It develops core concepts including probability axioms, repeated trials, functions of random variables, multiple random variables, sequences, and statistics.
3
It introduces stochastic-process concepts through random walks, spectral representation and estimation, mean-square estimation, entropy, and Markov models.
4
The work presents a comprehensive treatment of probability theory, random variables, and their associated mathematical foundations.
Research Object
probability, random variables, and stochastic processes
Research Subject
their fundamental concepts, statistical properties, spectral representations and estimation, entropy, Markov behavior, random walks, and queueing applications
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1984-12-01
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