Calculating Poisson Probabilities (DP IB Applications & Interpretation (AI): HL): Revision Note

Dan Finlay

Written by: Dan Finlay

Reviewed by: Roger B

Updated on

Calculating Poisson probabilities

Throughout this section we will use the random variable X~Po(m). For a Poisson distribution X, the probability of X taking a non-integer or negative value is always zero. Therefore, any values mentioned in this section for X will be assumed to be non-negative integers. However the value of m can be any real positive value (it doesn't need to be an integer).

How do I calculate P(X = x): the probability of a single value for a Poisson distribution?

  • You should have a GDC that can calculate Poisson probabilities

  • You want to use the "Poisson Probability Distribution" function

    • This is sometimes shortened to PPD, Poisson PD or Poisson Pdf

  • You will need to enter:

    • The 'x' value - the value of x for which you want to find P(X=x)

    • The 'λ' value - the mean number of occurrences (m)

Examiner Tips and Tricks

Note that many calculators will use λ for the mean of a Poisson distribution, instead of m.

  • Some calculators will give you the option of listing the probabilities for multiple values of x at once

  • There is a formula that you can use but you are expected to be able to use the distribution function on your GDC

    • P(X=x)=emmxx!

      • where e is Euler's constant

      • x!=x×(x1)××2×1 and 0!=1

How do I calculate P(a ≤ X ≤ b): the cumulative probabilities for a Poisson distribution? 

  • You should have a GDC that can calculate cumulative Poisson probabilities

    • Most calculators will find P(aXb)

    • Some calculators can only find P(Xb)

      • The identities below will help in this case

  • You should use the "Poisson Cumulative Distribution" function

    • This is sometimes shortened to PCD, Poisson CD or Poisson Cdf

  • You will need to enter:

    • The lower value - this is the value a

      • This can be zero in the case P(Xb)

    • The upper value - this is the value b

      • This can be a very large number (9999... or 1099) in the case P(Xa)

    • The 'λ' value - the mean number of occurrences (m)

How do I find probabilities if my GDC only calculates P(X ≤ x)?

  • To calculate P(Xx) just enter x into the cumulative distribution function

  • To calculate P(X < x) use:

    • P(X<x)=P(Xx1) which works when is a Poisson random variable

      • P(X < 5) = P(≤ 4)

  • To calculate P(X > x) use:

    • P(X>x)=1P(Xx) which works for any random variable

      • P(X > 5) = 1 - P(≤ 5)

  • To calculate P(Xx) use:

    • P(Xx)=1P(Xx1) which works when is a Poisson random variable

      • P(X ≥ 5) = 1 - P(≤ 4)

  • To calculate P(a Xb) use:

    • P(aXb)=P(Xb)P(Xa1) which works when is a Poisson random variable

      • P(5 ≤ ≤ 9) = P(≤ 9) - P(≤ 4)

What if an inequality does not have the equals sign (strict inequality)? 

  • For a Poisson distribution (as it is discrete) you could rewrite all strict inequalities (< and >) as weak inequalities (≤ and ≥) by using the identities for a Poisson distribution

    • P(X<x)=P(Xx1) and P(X>x)=P(Xx+1)

    • For example: P(X < 5) = P(X ≤ 4) and P(X > 5) = P(X ≥ 6)

  • It helps to think about the range of integers you want

    • Identify the smallest and biggest integers in the range

  • If your range has no minimum then use 0

    • P(Xb)=P(0Xb)

  • P(a<Xb)=P(a+1Xb)

    • P(5 < X ≤ 9) = P(6 ≤ X ≤ 9)

  • P(aX<b)=P(aXb1)

    • P(5 ≤ X < 9) = P(5 ≤ X ≤ 8)

  • P(a<X<b)=P(a+1Xb1)

    • P(5 < X < 9) = P(6 ≤ X ≤ 8)

Worked Example

The random variables X~Po(6.25) and Y~Po(4) are independent. Find:

i) P(X=5),

Answer:

4-10-2-ib-ai-hl-poisson-prob-a-we-solution

ii) P(Y5),

Answer:

4-10-2-ib-ai-hl-poisson-prob-b-we-solution

iii) P(X+Y>7).

Answer:

4-10-2-ib-ai-hl-poisson-prob-c-we-solution

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Dan Finlay

Author: Dan Finlay

Expertise: Portfolio Lead

Dan graduated from the University of Oxford with a First class degree in mathematics. As well as teaching maths for over 8 years, Dan has marked a range of exams for Edexcel, tutored students and taught A Level Accounting. Dan has a keen interest in statistics and probability and their real-life applications.

Roger B

Reviewer: Roger B

Expertise: Development Editor

Roger's teaching experience stretches all the way back to 1992, and in that time he has taught students at all levels between Year 7 and university undergraduate. Having conducted and published postgraduate research into the mathematical theory behind quantum computing, he is more than confident in dealing with mathematics at any level the exam boards might throw at you.