An Introduction to Probability Theory and Its Applications, Volume 2

Name: An Introduction to Probability Theory and Its Applications, Volume 2
Brand: Unknown
SKU: 9780471257097
Price: 541.99 NZD
Availability: InStock

By William Feller

Series: Wiley Series in Probability and Statistics

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Summary

An Introduction to Probability Theory and Its Applications, Volume 2 by William Feller delves into advanced topics in probability theory, offering a rigorous exploration of concepts such as stochastic processes, Markov chains, and the law of large numbers. With detailed examples and meticulously crafted explanations, the volume builds upon foundational theories to enhance understanding of complex statistical phenomena. This work is essential for anyone looking to deepen their comprehension of the mathematical underpinnings of probability.
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Format: Paperback / softback

$541⁹⁹

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An Introduction to Probability Theory and Its Applications, Volume 2

$541⁹⁹

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You might enjoy this book if you have a keen interest in the mathematical underpinnings of probability theory and its practical applications. This in-depth exploration is ideal for those who are academically inclined towards science and nature subjects and appreciate the rigorous development of probabilistic concepts.

An Introduction to Probability Theory and Its Applications, Volume 2

Description

The classic text for understanding complex statistical probability

An Introduction to Probability Theory and Its Applications offers comprehensive explanations to complex statistical problems. Delving deep into densities and distributions while relating critical formulas, processes, and approaches, this rigorous text provides a solid grounding in probability with practice problems throughout. Heavy on application without sacrificing theory, the discussion takes the time to explain difficult topics and how to use them.

This new second edition includes new material related to the substitution of probabilistic arguments for combinatorial artifices as well as new sections on branching processes, Markov chains, and the DeMoivre-Laplace theorem.