Layman's description of PDF and CDF












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Can anyone please explain PDF and CDF in simple words.



(Please don't define it from wiki.)






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    $begingroup$


    Can anyone please explain PDF and CDF in simple words.



    (Please don't define it from wiki.)






    machine-learning data-science-model






    share







    New contributor




    user241807 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







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      1





      $begingroup$


      Can anyone please explain PDF and CDF in simple words.



      (Please don't define it from wiki.)






      machine-learning data-science-model






      share







      New contributor




      user241807 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      Can anyone please explain PDF and CDF in simple words.



      (Please don't define it from wiki.)






      machine-learning data-science-model




      machine-learning data-science-model





      share







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      user241807 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.










      share







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      user241807 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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          $begingroup$

          First let's look at how these are related. The cumulative distribution function (CDF) is the cumulative sum of the probability density function (PDF).



          You take the sum of all the probabilities of the previous values.




          For example, if we roll a dice, then there is a 1/6th chance of getting any value. Thus the PDF is



          enter image description here



          Then the cumulative distribution function at any point is the sum of all the points before it. So the CDF of rolling a 2, is 2/6th, a 3 would be 3/6th. The graph looks like



          enter image description here




          Mathematically we define the CDF as $F$ as



          $F(x) = Pr(X < x)$.



          For our dice example



          $F(1) = P(X=1) = 1/6$



          $F(2) = P(X=1) + P(X=2) = 2/6$



          $F(3) = P(X=1) + P(X=2) + P(X=3) = 1/6 + 1/6 + 1/6 = 3/6$



          $F(4) = 4/6$



          $F(5) = 5/6$



          $F(6) = 1$.



          So you can see how we get the plot above.






          share|improve this answer









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            $begingroup$

            First let's look at how these are related. The cumulative distribution function (CDF) is the cumulative sum of the probability density function (PDF).



            You take the sum of all the probabilities of the previous values.




            For example, if we roll a dice, then there is a 1/6th chance of getting any value. Thus the PDF is



            enter image description here



            Then the cumulative distribution function at any point is the sum of all the points before it. So the CDF of rolling a 2, is 2/6th, a 3 would be 3/6th. The graph looks like



            enter image description here




            Mathematically we define the CDF as $F$ as



            $F(x) = Pr(X < x)$.



            For our dice example



            $F(1) = P(X=1) = 1/6$



            $F(2) = P(X=1) + P(X=2) = 2/6$



            $F(3) = P(X=1) + P(X=2) + P(X=3) = 1/6 + 1/6 + 1/6 = 3/6$



            $F(4) = 4/6$



            $F(5) = 5/6$



            $F(6) = 1$.



            So you can see how we get the plot above.






            share|improve this answer









            $endgroup$


















              1












              $begingroup$

              First let's look at how these are related. The cumulative distribution function (CDF) is the cumulative sum of the probability density function (PDF).



              You take the sum of all the probabilities of the previous values.




              For example, if we roll a dice, then there is a 1/6th chance of getting any value. Thus the PDF is



              enter image description here



              Then the cumulative distribution function at any point is the sum of all the points before it. So the CDF of rolling a 2, is 2/6th, a 3 would be 3/6th. The graph looks like



              enter image description here




              Mathematically we define the CDF as $F$ as



              $F(x) = Pr(X < x)$.



              For our dice example



              $F(1) = P(X=1) = 1/6$



              $F(2) = P(X=1) + P(X=2) = 2/6$



              $F(3) = P(X=1) + P(X=2) + P(X=3) = 1/6 + 1/6 + 1/6 = 3/6$



              $F(4) = 4/6$



              $F(5) = 5/6$



              $F(6) = 1$.



              So you can see how we get the plot above.






              share|improve this answer









              $endgroup$
















                1












                1








                1





                $begingroup$

                First let's look at how these are related. The cumulative distribution function (CDF) is the cumulative sum of the probability density function (PDF).



                You take the sum of all the probabilities of the previous values.




                For example, if we roll a dice, then there is a 1/6th chance of getting any value. Thus the PDF is



                enter image description here



                Then the cumulative distribution function at any point is the sum of all the points before it. So the CDF of rolling a 2, is 2/6th, a 3 would be 3/6th. The graph looks like



                enter image description here




                Mathematically we define the CDF as $F$ as



                $F(x) = Pr(X < x)$.



                For our dice example



                $F(1) = P(X=1) = 1/6$



                $F(2) = P(X=1) + P(X=2) = 2/6$



                $F(3) = P(X=1) + P(X=2) + P(X=3) = 1/6 + 1/6 + 1/6 = 3/6$



                $F(4) = 4/6$



                $F(5) = 5/6$



                $F(6) = 1$.



                So you can see how we get the plot above.






                share|improve this answer









                $endgroup$



                First let's look at how these are related. The cumulative distribution function (CDF) is the cumulative sum of the probability density function (PDF).



                You take the sum of all the probabilities of the previous values.




                For example, if we roll a dice, then there is a 1/6th chance of getting any value. Thus the PDF is



                enter image description here



                Then the cumulative distribution function at any point is the sum of all the points before it. So the CDF of rolling a 2, is 2/6th, a 3 would be 3/6th. The graph looks like



                enter image description here




                Mathematically we define the CDF as $F$ as



                $F(x) = Pr(X < x)$.



                For our dice example



                $F(1) = P(X=1) = 1/6$



                $F(2) = P(X=1) + P(X=2) = 2/6$



                $F(3) = P(X=1) + P(X=2) + P(X=3) = 1/6 + 1/6 + 1/6 = 3/6$



                $F(4) = 4/6$



                $F(5) = 5/6$



                $F(6) = 1$.



                So you can see how we get the plot above.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 12 hours ago









                JahKnowsJahKnows

                4,702525




                4,702525






















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