Overview

The probability section of this guide will likely never be fully completed, due to the fact that the Prob 140 textbook is such an excellent resource in probability theory. Go read it and do the problems!
Instead of a full write-up, the pages in this section will typically just link to relevant sections from the textbook. Personally, I found everything I needed to do well in CS70 probability (and much more) here, including examples that are very similar to problems you might see on the homework.
TL;DR don't use this section of the guide, just read the 140 textbook.
Here is a running list of topics in this section:
​Counting provides us an intuitive method of figuring out how many possible ways there are to do something.
​Discrete probability distributions, such as the Binomial or Geometric distributions, describe the probabilities of a finite set of outcomes.
​Continuous probability distributions, such as the Poisson or Normal distributions, help us model real values, like lifetime or height.
​Markov chains model transitions between discrete states.
​Expectation and variance are tools to describe the characteristics of a random variable or distribution.
​Concentration inequalities allow us to approximate bounds for random variables when we only know their expectation and/or variance.
There is far more to explore in learning the basics of probability- not everything is included in this list!
Reference
​http://prob140.org/assets/final_reference_fa18.pdf​
Distribution
Values
Density
Expectation
Variance
Links
Uniform(m,n)
[m, n]
​1n−m+1\frac{1}{n-m+1}n−m+11​
​
​m+n2\frac{m+n}{2}2m+n​
​
​(n−m+1)2−112\frac{(n-m+1)^2-1}{12}12(n−m+1)2−1​
​
​
Bernoulli(p)
Indicator
0, 1
P(X=1) = p
P(X=0) = 1-p
​ppp
​
​p(1−p)p(1-p)p(1−p)
​
​
Binomial(n,p)
[0, n]
​(nk)pkqn−k\binom{n}{k}p^kq^{n-k}(kn​)pkqn−k
​
​npnpnp
​
​np(1−p)np(1-p)np(1−p)
​
​
Poisson(μ\muμ
)
​x≥0x\ge0x≥0
​
​e−μμkk!e^{-\mu}\frac{\mu^k}{k!}e−μk!μk​
​
​μ\muμ
​
​μ\muμ
​
​
Geometric(p)
​x≥1x \ge 1x≥1
​
​(1−p)kp(1-p)^kp(1−p)kp
​
​1p\frac{1}{p}p1​
​
​1−pp2\frac{1-p}{p^2}p21−p​
​
​
Hypergeom.(N,G,n)
[0, n]
​(Gg)(Bb)(Nn)\frac{\binom{G}{g}\binom{B}{b}}{\binom{N}{n}}(nN​)(gG​)(bB​)​
​
​nGNn\frac{G}{N}nNG​
​
​nGNBNN−nN−1n\frac{G}{N}\frac{B}{N}\frac{N-n}{N-1}nNG​NB​N−1N−n​
​
​
Uniform Continuous
(a, b)
​1b−a\frac{1}{b-a}b−a1​
​
​a+b2\frac{a+b}{2}2a+b​
​
​(b−a)212\frac{(b-a)^2}{12}12(b−a)2​
​
​
Beta(r,s)
(0, 1)
​Γ(r+s)Γ(r)Γ(s)xr−1(1−x)s−1\frac{\Gamma(r+s)}{\Gamma(r)\Gamma(s)}x^{r-1}(1-x)^{s-1}Γ(r)Γ(s)Γ(r+s)​xr−1(1−x)s−1
​
​rr+s\frac{r}{r+s}r+sr​
​
​rs(r+s)2(r+s)\frac{rs}{(r+s)^2(r+s)}(r+s)2(r+s)rs​
​
​
Exponential(λ\lambdaλ
)
(Gamma(1, λ\lambdaλ
))
​x≥0x\ge0x≥0
​
​λe−λx\lambda e^{-\lambda x}λe−λx
​
​1λ\frac{1}{\lambda}λ1​
​
​1λ2\frac{1}{\lambda^2}λ21​
​
​
Gamma(r, λ\lambdaλ
)
​x≥0x\ge0x≥0
​
​λrΓ(r)xr−1eλx\frac{\lambda^r}{\Gamma(r)}x^{r-1}e^{\lambda x}Γ(r)λr​xr−1eλx
​
​rλ\frac{r}{\lambda}λr​
​
​rλ2\frac{r}{\lambda^2}λ2r​
​
​
Normal(0,1)
​x∈Rx \in \mathbb{R}x∈R
​
​12πe−12x2\frac{1}{\sqrt{2\pi}}e^{-\frac{1}{2}x^2}2π​1​e−21​x2
​
0
1
​
Where (nk)=n!k!(n−k)!\binom{n}{k} = \frac{n!}{k!(n-k)!}(kn​)=k!(n−k)!n!​
and Γ(r)=∫0∞xr−1e−xdx=(r−1)Γ(r−1)=(r−1)!\Gamma(r) = \int_0^\infty x^{r-1}e^{-x}dx = (r-1)\Gamma(r-1) = (r-1)!Γ(r)=∫0∞​xr−1e−xdx=(r−1)Γ(r−1)=(r−1)!
​

Distribution	Values	Density	Expectation	Variance
Uniform(m,n)	[m, n]	$\frac{1}{n-m+1}$	$\frac{m+n}{2}$	$\frac{(n-m+1)^2-1}{12}$
Bernoulli(p) Indicator	0, 1	P(X=1) = p P(X=0) = 1-p	$p$	$p(1-p)$
Binomial(n,p)	[0, n]	$\binom{n}{k}p^kq^{n-k}$	$np$	$np(1-p)$
Poisson( $\mu$ )	$x\ge0$	$e^{-\mu}\frac{\mu^k}{k!}$	$\mu$	$\mu$
Geometric(p)	$x \ge 1$	$(1-p)^kp$	$\frac{1}{p}$	$\frac{1-p}{p^2}$
Hypergeom.(N,G,n)	[0, n]	$\frac{\binom{G}{g}\binom{B}{b}}{\binom{N}{n}}$	$n\frac{G}{N}$	$n\frac{G}{N}\frac{B}{N}\frac{N-n}{N-1}$
Uniform Continuous	(a, b)	$\frac{1}{b-a}$	$\frac{a+b}{2}$	$\frac{(b-a)^2}{12}$
Beta(r,s)	(0, 1)	$\frac{\Gamma(r+s)}{\Gamma(r)\Gamma(s)}x^{r-1}(1-x)^{s-1}$	$\frac{r}{r+s}$	$\frac{rs}{(r+s)^2(r+s)}$
Exponential( $\lambda$ ) (Gamma(1, $\lambda$ ))	$x\ge0$	$\lambda e^{-\lambda x}$	$\frac{1}{\lambda}$	$\frac{1}{\lambda^2}$
Gamma(r, $\lambda$ )	$x\ge0$	$\frac{\lambda^r}{\Gamma(r)}x^{r-1}e^{\lambda x}$	$\frac{r}{\lambda}$	$\frac{r}{\lambda^2}$
Normal(0,1)	$x \in \mathbb{R}$	$\frac{1}{\sqrt{2\pi}}e^{-\frac{1}{2}x^2}$	0	1

Last modified 1yr ago