An Introduction to Probabilistic Modeling / by Pierre Brémaud.
Introduction to the basic concepts of probability theory: independence, expectation, convergence in law and almost-sure convergence. Short expositions of more advanced topics such as Markov Chains, Stochastic Processes, Bayesian Decision Theory and Information Theory.
Saved in:
| Online Access: |
Full Text (via Springer) |
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| Main Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
New York, NY :
Springer New York,
1988.
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| Series: | Undergraduate texts in mathematics.
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| Subjects: |
MARC
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| 245 | 1 | 3 | |a An Introduction to Probabilistic Modeling / |c by Pierre Brémaud. |
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| 505 | 0 | |a Preface -- Abbreviations and Notations -- Basic Concepts and Elementary Models -- Discrete Probability -- Probability Densities -- Gaus and Poisson -- Convergences -- Additional Exercises -- Solutions to Additional Exercises -- Index. | |
| 520 | |a Introduction to the basic concepts of probability theory: independence, expectation, convergence in law and almost-sure convergence. Short expositions of more advanced topics such as Markov Chains, Stochastic Processes, Bayesian Decision Theory and Information Theory. | ||
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