总结自 Coursera lecture Statistical Inference section 02 Probability。
1. Notation
Symbol  Definition  Example 

$\Omega$  The sample space, the collection of possible outcomes of an experiment  Die roll, $\Omega = \lbrace 1,2,3,4,5,6 \rbrace$ 
$E$  An event, a subset of sample space  Die roll is even, $E = \lbrace 2,4,6 \rbrace$ 
$\omega$  An elementary or simple event, a particular result of an experiment  Die roll is a four, $\omega = 4$ 
$\phi$  The null event or the empty set 
2. Interpretation of set operations
1.$\omega \in E$ implies that $E$ occurs when$\omega$occurs 2.$\omega \notin E$ implies that $E$ does not occur when $\omega$ occurs 3.$E \subset F$ implies that the occurrence of $E$ implies the occurrence of $F$ 4.$E \cap F$ implies the event that both $E$ and $F$ occur 5.$E \cup F$ implies the event that at least one of $E$ or $F$ occur 6.$E \cap F = \phi$ means that $E$ and $F$ are mutually exclusive, or cannot both occur 7.$E^c$ or $\bar{E}$ is the event that $E$ does not occur
3. Probability
A probability measure, $P$, is a function from the collection of possible events so that the following hold:
 For an event $E \subset \Omega$, $0 \leq P(E) \leq 1$

$P(\Omega) = 1$
 If $E_1$ and $E_2$ are mutually exclusive events, $P(E_1 \cup E_2) = P(E_1) + P(E_2)$.
Part 3 of the definition implies finite additivity: $P(\bigcup_{i=1}^{n} A_i) = \sum_{i=1}^n P(A_i)$, where the $A_i$ are mutually exclusive.
4. Consequences (推论)

$P(\phi) = 0$

$P(E) = 1  P(E^c)$
 If $A \subset B$, then $P(A) \leq P(B)$

$P(A \cup B) = P(A) + P(B)  P(A \cap B) = 1  P(A^c \cap B^c)$

$P(A \cap B^c) = P(A)  P(A \cap B)$

$P(\bigcup_{i=1}^{n} E_i) \leq \sum_{i=1}^n P(E_i)$

$P(\bigcup_{i=1}^{n} E_i) \geq max(P(E_i))$
5. Random variables
 A random variable is a numerical outcome of an experiment.
 2 varieties
 Discrete
 Continuous
 Discrete random variable are random variables that take on only a countable number of possibilities.

$P(X = k)$
 described by PMF

 Continuous random variable can take any value on the real line or some subset of the real line.

$P(X \in A)$
 discribed by PDF and CDF

6. PMF: Probability Mass Function
A PMF evaluated at a value corresponds to the probability that a random variable takes that value (我觉得你可以理解为 $p(x) = P(X = x)$).
To be a valid PMF, $p$ must satisfy:
1.$p(x) \geq 0$ for all $x$ 2.$\sum_{x} p(x) = 1$ (The sum is taken over all of the possible values for $x$)
E.g., let $X$ be the result of a coin flip where $X=0$ represents tails and $X=1$ represents heads. Suppose that we do not know whether or not the coin is fair. Let $\theta$ be the probability of a head expressed as a proportion (between 0 and 1). Then we get:
\[p(x) = \theta^x (1  \theta)^{1x},\, \text{for x = 0,1}\]PMF 其实就是分布律，用来描述 discrete random variable
7. PDF: Probability Density Function
A PDF, is a function associated with a continuous random variable.
Areas under the PDF correspond to probabilities for that random variable.
To be a valid PDF, $f$ must satisfy:
1.$f(x) \geq 0$ for all $x$
 The area under $f(x)$ is 1
实际有：$P [a \le X \le b] = \int_a^b f(x) \, dx$
8. CDF: Cumulative Distribution Function & SF: Survival Function
The CDF of a random variable $X$ is defined as the function:
\[F(x) = P(X \leq x)\]This definition applies regardless of whether is discrete or continuous.
The SF of a random variable $X$ is defined as:
\[S(x) = P(X > x)\]Notice that $S(x) = 1  F(x)$
For continuous random variables, the PDF is the derivative of the CDF, i.e. $f(x) = F’(x)$
9. Example: Beta Distribution
More details in wikipedia Beta distribution.
If $x \in [0, 1]$ and $X \sim Beta(\alpha,\beta)$, 我们称 $X$ 足 $\alpha, \beta$ 控制的 beta 分布。$\alpha, \beta > 0$ and are both real number, a.k.a “shape parameters”.
PDF is defined as $f(x \vert \alpha,\beta) = \frac{x^{\alpha1} (1x)^{\beta1}}{B(\alpha, \beta)}$
where constant $B(\alpha, \beta) = \int_{0}^{1} t^{\alpha1} (1t)^{\beta1} dt$
or with Gamma Funtion $\Gamma(n) = \begin{cases} (n1)! & \text { n is a positive integer } \newline \int_{0}^{1} t^{\alpha1} (1t)^{\beta1} dt & \text { n is real or complex } \end{cases}$
we can rewrite $B(\alpha, \beta) = \frac{\Gamma(\alpha) \Gamma(\beta)}{\Gamma(\alpha + \beta)}$
If $\alpha = 2$ and $\beta = 1$, then $B(\alpha, \beta) = \frac{1!\times0!}{2!} = 0.5$ or $B(\alpha, \beta) = \int_{0}^{1} t \cdot dt = \frac{1}{2}t^2 \vert _{0}^{1} = 0.5$, therefore $f(x) = 2x$ and $F(x) = x^2$.
我们在 R 中执行 pbeta(0.75, 2, 1)
会得到 0.5625。根据 R Generating Random Numbers and Random Sampling 里总结的规律，p 开头的都是求 CDF，i.e. $F(x)$，所以这里 pbeta(0.75, 2, 1)
的意义就是：当 $\alpha = 2$ and $\beta = 1$ 时，求 $F(0.75)$。最终得到 $F(0.75) = 0.5625$。这和我们用 $f(x)$ 的面积来算的结果是一致的：
> x < c(0, 1, 1, 1.5)
> y < c(0, 2, 0, 0)
> plot(x, y, lwd = 3, frame = FALSE, type = "l", ylab="f(x)")
> abline(h=1.5, v=0.75)