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2: Probability Spaces

Last updated

May 5, 2020
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- 1.12: Special Set Structures
- 2.1: Random Experiments

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The basic topics in this chapter are fundamental to probability theory, and should be accessible to new students of probability. We start with the paradigm of the random experiment and its mathematical model, the probability space. The main objects in this model are sample spaces, events, random variables, and probability measures. We also study several concepts of fundamental importance: conditional probability and independence.

The advanced topics can be skipped if you are a new student of probability, or can be studied later, as the need arises. These topics include the convergence of random variables, the measure-theoretic foundations of probability theory, and the existence and construction of probability measures and random processes.

2.1: Random Experiments
2.2: Events and Random Variables
2.3: Probability Measures
This section contains the final and most important ingredient in the basic model of a random experiment.
2.4: Conditional Probability
The purpose of this section is to study how probabilities are updated in light of new information, clearly an absolutely essential topic. If you are a new student of probability, you may want to skip the technical details.
2.5: Independence
In this section, we will discuss independence, one of the fundamental concepts in probability theory. Independence is frequently invoked as a modeling assumption, and moreover, (classical) probability itself is based on the idea of independent replications of the experiment. As usual, if you are a new student of probability, you may want to skip the technical details.
2.6: Convergence
2.7: Measure Spaces
2.8: Existence and Uniqueness
2.9: Probability Spaces Revisited
2.10: Stochastic Processes
2.11: Filtrations and Stopping Times

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