An incomplete guide to probability theory

This is a brief introduction/cheatsheet to probability theory. Most of the content is from the book “Statistical Inference” 1


I assume you have some basic ideas of set theory such as what is intersection and what is union.

1.1 \(\sigma\)-algebra and measure theory

You probably don’t need to know this. But in case you need it, here is the definition of \(\sigma\)-algebra.

A collection of subsets \(\mathcal{S}\) is called sigma algebra, denoted by \(\mathcal{B}\), if it satisfies the follwoing three conditions:

  1. \(\emptyset \in \mathcal{B}\) (the empty set is an element of \(\mathcal{B}\))
  2. If \(\mathcal{A} \in \mathcal{B}\), then \(\mathcal{A}^{c} \in \mathcal{B}\)
  3. If \(\mathcal{A}_{1}, \mathcal{A}_{2},... \in \mathcal{B}\), then \(\cup_{i=1}^{\infty} \mathcal{A}_{i} \in \mathcal{B}\)

1.2 Rules of probability

The definition of probability function:

Given a sample space \(\mathcal{S}\) and an associated sigma algebra \(\mathcal{B}\), a probability function satisfies:

  1. \(\mathcal{P}(\mathcal{A}) \geq 0\) for all \(\mathcal{A} \in \mathcal{B}\).
  2. \(\mathcal{P}(\mathcal{S}) = 1\).
  3. If \(\mathcal{A}_{1}, \mathcal{A}_{2}, ... \in \mathcal{B}\) are pairwise disjoint, then \(\mathcal{P}(\cup_{i=1}^{\infty} \mathcal{A}_{i}) = \sum_{i=1}^{\infty} \mathcal{P}(A_{i})\).

Some calculus of probability:

If \(\mathcal{P}\) is a probabilty function and \(\mathcal{A}\) is any set in \(\mathcal{B}\), then

  1. \(\mathcal{P}(\emptyset) = 0\).
  2. \(\mathcal{P}(A) \leq 1\).
  3. \(\mathcal{P}(\mathcal{A^{c}}) = 1 - \mathcal{P}(\mathcal{A})\).
  4. \(\mathcal{P} (\mathcal{A} \cup \mathcal{B}) = \mathcal{P}(\mathcal{A}) + \mathcal{P}(\mathcal{B}) - \mathcal{P} (\mathcal{A} \cap \mathcal{B})\).
  5. \(\mathcal{P} (\mathcal{B} \cap \mathcal{A}^{c}) = \mathcal{P}(\mathcal{B}) - \mathcal{P} (\mathcal{A} \cap \mathcal{B})\).
  6. If \(\mathcal{A} \subseteq \mathcal{B}\), then \(\mathcal{P}(\mathcal{A}) \leq \mathcal{P}(\mathcal{B})\).

1.3 Independence

1.4 Random variables

1.5 Distribution functions

1.6 Densities

1.7 Some inequalities

  1. Bonferroni inequality
  2. Jensen’s inequality

2. Transformation and Expectation

3. Common family of distribution

4. Multivariate distributions

5. Convergence in probability

You probably don’t need to know too much details about this.

  1. Casella, G., & Berger, R. L. (2021). Statistical inference. Cengage Learning. 

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