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Submitted To:
Maam Wajiha Nasir
Submitted By:
Muhammad Irfan Hussain
Roll # 39
Bs 3rd
Semester(Regular)
Binomial probability distribution:
Hyper geometric probability distribution
Poisson distribution
Poisson process
Negative binomial distribution
𝒇( 𝒙) = 𝒑( 𝑿 = 𝒙) = (
𝒏
𝒙
) 𝒑 𝒙
𝒒 𝒏−𝒙
, 𝒙
= 𝟎, 𝟏, 𝟐, … … … . . , 𝒏,
H( 𝒙; 𝑵, 𝒏, 𝒌) =
( 𝒌
𝒙)( 𝑵−𝒌
𝒏−𝒙)
( 𝑵
𝒏
)
, for x such that 0≤ 𝒙 ≥ 𝒏
𝐥𝐢𝐦𝒏→𝒙
𝒑→𝟎
𝒃( 𝒙; 𝒏, 𝒑) =
𝝁 𝒙
𝒆−𝝁
𝒙!
, 𝒙 = 𝟎, 𝟏, 𝟐 … … … ,∞
P( 𝒙; 𝞴𝒕) =
𝒆−𝞴𝒕( 𝞴𝒕) 𝒙
𝒙!
P(X=x)= ( 𝒙 − 𝟏
𝒌 − 𝟏
) 𝒑 𝒌
𝒒 𝒙−𝒌
, 𝒘𝒉𝒆𝒓𝒆 𝒙 = 𝒌 + 𝟏, 𝒌 + 𝟐, … … …
Geometric distribution
Multinomial distribution
Uniform distribution
Exponential distribution
𝒑( 𝑿 = 𝒙) = 𝒒 𝒙−𝟏
𝒑 , 𝒙=𝟏, 𝟐, 𝟑, … … … … . ∞
𝒑( 𝑿 𝟏=𝒙 𝟏,
𝑿 𝟐=𝒙 𝟐
… … … . 𝑿 𝑲=𝒙 𝒌
) =
𝒏!
𝒙 𝟏!, 𝒙 𝟐!, 𝒙 𝟑!, … … 𝒙 𝒌!
( 𝒙) = ∫
𝟏
𝑩 − 𝑨
𝑩
𝑨
, 𝑨 ≤ 𝑿 ≥ 𝑩,
𝒇( 𝒙) =
𝟏
𝜷
𝒆
−
𝒙
𝜷 , 𝟎 ≤ 𝒙 ≥ ∞
Normal distribution
Prepared by MUHAMMAD IRFAN HUSSAIN
Bs 3rd Semester 039 (Regular)
UNIVERSITY OF SARGODHA
 𝒇( 𝒙; 𝝁, 𝝈 𝟐) =
𝟏
𝝈√ 𝟐𝝅
𝒆
−
𝟏
𝟐𝝈 𝟐
( 𝒙−𝝁) 𝟐
− ∞ ≤ 𝒙 ≥ ∞
 𝒇( 𝒛; 𝒐, 𝟏) =
𝟏
√ 𝟐𝝅
𝒆
−
𝟏
𝟐𝝈 𝟐 𝒛 𝟐
𝟎 ≤ 𝒛 ≥ 𝟏
 𝒛 =
𝑿−𝝁
𝝈

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Prob dist pdf

  • 1. Submitted To: Maam Wajiha Nasir Submitted By: Muhammad Irfan Hussain Roll # 39 Bs 3rd Semester(Regular)
  • 2. Binomial probability distribution: Hyper geometric probability distribution Poisson distribution Poisson process Negative binomial distribution 𝒇( 𝒙) = 𝒑( 𝑿 = 𝒙) = ( 𝒏 𝒙 ) 𝒑 𝒙 𝒒 𝒏−𝒙 , 𝒙 = 𝟎, 𝟏, 𝟐, … … … . . , 𝒏, H( 𝒙; 𝑵, 𝒏, 𝒌) = ( 𝒌 𝒙)( 𝑵−𝒌 𝒏−𝒙) ( 𝑵 𝒏 ) , for x such that 0≤ 𝒙 ≥ 𝒏 𝐥𝐢𝐦𝒏→𝒙 𝒑→𝟎 𝒃( 𝒙; 𝒏, 𝒑) = 𝝁 𝒙 𝒆−𝝁 𝒙! , 𝒙 = 𝟎, 𝟏, 𝟐 … … … ,∞ P( 𝒙; 𝞴𝒕) = 𝒆−𝞴𝒕( 𝞴𝒕) 𝒙 𝒙! P(X=x)= ( 𝒙 − 𝟏 𝒌 − 𝟏 ) 𝒑 𝒌 𝒒 𝒙−𝒌 , 𝒘𝒉𝒆𝒓𝒆 𝒙 = 𝒌 + 𝟏, 𝒌 + 𝟐, … … …
  • 3. Geometric distribution Multinomial distribution Uniform distribution Exponential distribution 𝒑( 𝑿 = 𝒙) = 𝒒 𝒙−𝟏 𝒑 , 𝒙=𝟏, 𝟐, 𝟑, … … … … . ∞ 𝒑( 𝑿 𝟏=𝒙 𝟏, 𝑿 𝟐=𝒙 𝟐 … … … . 𝑿 𝑲=𝒙 𝒌 ) = 𝒏! 𝒙 𝟏!, 𝒙 𝟐!, 𝒙 𝟑!, … … 𝒙 𝒌! ( 𝒙) = ∫ 𝟏 𝑩 − 𝑨 𝑩 𝑨 , 𝑨 ≤ 𝑿 ≥ 𝑩, 𝒇( 𝒙) = 𝟏 𝜷 𝒆 − 𝒙 𝜷 , 𝟎 ≤ 𝒙 ≥ ∞
  • 4. Normal distribution Prepared by MUHAMMAD IRFAN HUSSAIN Bs 3rd Semester 039 (Regular) UNIVERSITY OF SARGODHA  𝒇( 𝒙; 𝝁, 𝝈 𝟐) = 𝟏 𝝈√ 𝟐𝝅 𝒆 − 𝟏 𝟐𝝈 𝟐 ( 𝒙−𝝁) 𝟐 − ∞ ≤ 𝒙 ≥ ∞  𝒇( 𝒛; 𝒐, 𝟏) = 𝟏 √ 𝟐𝝅 𝒆 − 𝟏 𝟐𝝈 𝟐 𝒛 𝟐 𝟎 ≤ 𝒛 ≥ 𝟏  𝒛 = 𝑿−𝝁 𝝈