University of Washington  
Department of Mathematics  
MATH 394: Probability I  
Probability Distribution Reference Sheet  
MATH 394: Probability I (Summer 2026)  
Instructor: Arman Jahangiri, Department of Mathematics @ University of Washington  
Discrete Distributions I  
Quantity  
Bernoulli  
Binomial  
Poisson  
Geometric  
Geometric,  
zero-based  
Parameters  
PMF  
0 ≤ p ≤ 1  
n ≥ 1, 0 ≤ p ≤ 1  
λ > 0  
0 < p ≤ 1  
0 < p ≤ 1  
λk  
P(X = k) =  
pk(1 − p)1−k  
k = 0, 1  
P(X = k) =  
pk(1 − p)n−k  
k = 0, . . . , n  
P(X = k) = e−λ  
P(X = k) = p(1−p)k−1 P(X = k) = p(1 − p)k  
ꢀ ꢁ  
n
k
k!  
Support  
Mean  
k = 0, 1, 2, . . .  
k = 1, 2, . . .  
1
k = 0, 1, 2, . . .  
1 − p  
p
np  
λ
p
p
1 − p  
1 − p  
Variance  
MGF  
p(1 − p)  
np(1 − p)  
λ
p2  
p2  
p
pet  
1 − p + pet  
(1 − p + pet)n  
exp{λ(et − 1)}  
1 − (1 − p)et  
1 − (1 − p)et  
Discrete Distributions II  
Quantity  
Negative Binomial  
Negative Binomial,  
failures before r successes  
Hypergeometric  
Negative Hypergeometric  
Parameters  
PMF  
r ≥ 1, 0 < p ≤ 1  
r ≥ 1, 0 < p ≤ 1  
w, b, n  
w, b, r  
ꢀ ꢁꢀ  
ꢁ
w
b
k
n−k  
P(X = k) =  
pr(1 − p)k−r  
P(X = k) =  
P(X = k) =  
P(X = k) =  
r+k−1 w+b−r−k  
ꢀ
ꢁ
w+b  
n
ꢀ
ꢁ
ꢀ
ꢁ
ꢀ
ꢁꢀ  
ꢁ
k−1  
r+k−1  
pr(1 − p)k  
r−1  
r−1  
r−1  
w−r  
ꢀ
ꢁ
w+b  
w
Support  
Mean  
k = r, r + 1, . . .  
k = 0, 1, 2, . . .  
max(0, n − b) ≤ k ≤ min(n, w)  
k = 0, 1, . . . , b  
r
r(1 − p)  
nw  
rb  
p
p
w + b  
w
w + 1  
rb(w + b + 1)(w − r + 1)  
ꢂ
ꢃ
r(1 − p)  
r(1 − p)  
w + b − n  
w
Variance  
MGF  
n
1 −  
p2  
pet  
p2  
w + b − 1 w + b  
w + b  
(w + 1)2(w + 2)  
ꢀ ꢁꢀ  
ꢁ
ꢀ
ꢁꢀ  
ꢁ
ꢂ
ꢃ
ꢂ
ꢃ
w
k
ꢀ
b
r+k−1 w+b−r−k  
b
r
r
X
X
p
n−k  
r−1  
w−r  
etk  
etk  
ꢁ
ꢀ
ꢁ
w+b  
n
w+b  
w
1 − (1 − p)et  
1 − (1 − p)et  
k
k=0  
© 2026 Arman Jahangiri — Department of Mathematics @ University of Washington.  
For educational use in MATH 394: Probability I  
Summer 2026  
Arman Jahangiri  
1 / 2  
University of Washington  
Department of Mathematics  
MATH 394: Probability I  
Continuous Distributions I  
Quantity  
Uniform  
Normal  
Log-Normal  
Exponential  
Gamma  
Parameters  
PDF  
a < b  
µ ∈ R, σ2 > 0  
fX (x) =  
µ ∈ R, σ2 > 0  
fX (x) =  
λ > 0  
r > 0, λ > 0  
1
fX (x) =  
fX (x) = λe−λx  
fX (x) =  
ꢄ
ꢅ
ꢄ
x > 0  
ꢅ
λr  
b − a  
1
(x − µ)2  
1
(log x − µ)2  
xr−1e−λx  
Γ(r)  
√
exp  
−
√
exp  
−
2σ2  
2σ2  
2πσ2  
xσ 2π  
Support  
Mean  
a < x < b  
a + b  
−∞ < x < ∞  
x ≥ 0  
x ≥ 0  
1
r
2
/2  
µ
eµ+σ  
2
λ
1
λ2  
λ
λ
r
(b − a)2  
2
2
Variance  
MGF  
σ2  
e2µ+σ (eσ − 1)  
12  
λ2  
ꢄ
ꢅ
ꢂ
ꢃ
r
etb − eta  
σ2t2  
λ
exp µt +  
2
DNE  
t(b − a)  
λ − t  
λ − t  
Continuous Distributions II  
Quantity  
Beta  
Weibull  
Chi-Square  
Student-t  
Parameters  
PDF  
a, b > 0  
α, λ > 0  
n > 0  
n > 0  
α
fX (x) =  
fX (x) = αλxα−1e−λx  
fX (x) =  
fX (x) =  
ꢂ
ꢃ−(n+1)/2  
1
Γ((n + 1)/2)  
√
x2  
n
Γ(a + b)  
xn/2−1e−x/2  
xa−1(1 − x)b−1  
1 +  
2n/2Γ(n/2)  
Γ(a)Γ(b)  
nπΓ(n/2)  
Support  
Mean  
0 < x < 1  
x > 0  
x > 0  
−∞ < x < ∞  
0, n > 1  
a
Γ(1 + 1/α)  
n
a + b  
ab  
(a + b)2(a + b + 1)  
λ1/α  
Γ(1 + 2/α)  
− µ2  
n
Variance  
MGF  
2n  
, n > 2  
n − 2  
DNE  
λ2/α  
–
–
(1 − 2t)−n/2  
Moment-Generating Function  
The moment-generating function is  
MX(t) = E(etX).  
When it exists near t = 0, it can be used to compute moments:  
E(Xn) = MX(n)(0).  
© 2026 Arman Jahangiri — Department of Mathematics @ University of Washington.  
For educational use in MATH 394: Probability I  
Summer 2026  
Arman Jahangiri  
2 / 2