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This introduction can be used, at the beginning graduate level, for a one-semester course on probability theory or for self-direction without benefit of a formal course; the measure theory needed is developed in the text. It will also be useful for students and teachers in related areas such as finance theory, electrical engineering, and operations research. The text covers the essentials in a directed and lean way with 28 short chapters, and assumes only an undergraduate background in mathematics. Readers are taken right up to a knowledge of the basics of Martingale Theory, and the interested student will be ready to continue with the study of more advanced topics, such as Brownian Motion and Ito Calculus, or Statistical Inference.
This introduction can be used, at the beginning graduate level, for a one-semester course on probability theory or for self-direction without benefit of a forma
This introduction can be used, at the beginning graduate level, for a one-semester course on probability theory or for self-direction without benefit of a forma
REAs Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its
Essentials of Probability Theory for Statisticians provides graduate students with a rigorous treatment of probability theory, with an emphasis on results centr
This classic introduction to probability theory for beginning graduate students covers laws of large numbers, central limit theorems, random walks, martingales,