Probability distribution vs. probability mass function (PMF): what is the difference between the terms?












4














Consider a discrete case. PMF is the probability each value of random variable gets. So, for example, X ~ Poisson(2). I plot these probabilities (below), so I can say that I show the PMF of X. But on the other hand I show the distribution of X. For example, I can say whether the distribution I have is symmetrical or not. So, what is the difference between probability distribution and PMF terms (in discrete case)? Below I also bring the definitions from Wikipedia, but it is not helpful either.



Many thanks!



Enter image description here



A probability mass function (pmf) is a function that gives the probability that a discrete random variable is exactly equal to some value.



A probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment.










share|cite|improve this question





























    4














    Consider a discrete case. PMF is the probability each value of random variable gets. So, for example, X ~ Poisson(2). I plot these probabilities (below), so I can say that I show the PMF of X. But on the other hand I show the distribution of X. For example, I can say whether the distribution I have is symmetrical or not. So, what is the difference between probability distribution and PMF terms (in discrete case)? Below I also bring the definitions from Wikipedia, but it is not helpful either.



    Many thanks!



    Enter image description here



    A probability mass function (pmf) is a function that gives the probability that a discrete random variable is exactly equal to some value.



    A probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment.










    share|cite|improve this question



























      4












      4








      4







      Consider a discrete case. PMF is the probability each value of random variable gets. So, for example, X ~ Poisson(2). I plot these probabilities (below), so I can say that I show the PMF of X. But on the other hand I show the distribution of X. For example, I can say whether the distribution I have is symmetrical or not. So, what is the difference between probability distribution and PMF terms (in discrete case)? Below I also bring the definitions from Wikipedia, but it is not helpful either.



      Many thanks!



      Enter image description here



      A probability mass function (pmf) is a function that gives the probability that a discrete random variable is exactly equal to some value.



      A probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment.










      share|cite|improve this question















      Consider a discrete case. PMF is the probability each value of random variable gets. So, for example, X ~ Poisson(2). I plot these probabilities (below), so I can say that I show the PMF of X. But on the other hand I show the distribution of X. For example, I can say whether the distribution I have is symmetrical or not. So, what is the difference between probability distribution and PMF terms (in discrete case)? Below I also bring the definitions from Wikipedia, but it is not helpful either.



      Many thanks!



      Enter image description here



      A probability mass function (pmf) is a function that gives the probability that a discrete random variable is exactly equal to some value.



      A probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment.







      probability probability-distributions terminology






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      share|cite|improve this question













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      share|cite|improve this question








      edited 9 hours ago









      Adam Hrankowski

      2,065928




      2,065928










      asked 13 hours ago









      John

      1156




      1156






















          2 Answers
          2






          active

          oldest

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          2














          The word "distribution" gets thrown around loosely sometimes, which can cause confusion.



          The distribution of a random variable $X$ is the function that takes a set $S subset mathbb R$ as input and returns the number $P(X in S)$ as output. (Technically I should assume that $S$ is a "nice" subset of $mathbb R$ in some sense, but let's not worry about that.) I think the Wikipedia article would be more clear if it just gave us this definition up front.



          The PMF of a random variable $X$ is the function that takes a number $x in mathbb R$ as input and returns the number $P(X=x)$ as output. If $X$ is a discrete random variable, then the PMF of $X$ is a convenient way to specify the distribution of $X$.



          Here is one way to describe the relationship between the distribution of $X$ and the PMF of $X$, in the case where $X$ is a discrete random variable. Suppose that the possible values of $X$ are $x_1,x_2,ldots$ If $f$ is the distribution of $X$, then
          $$
          f(S) = sum_{i : x_i in S} P(X = x_i)
          $$

          for any set $S subset mathbb R$.






          share|cite|improve this answer



















          • 1




            Thanks. But is not the definition of PMF by itself exactly as you described for "distribution of a random variable X"? If not, how they are different?
            – John
            13 hours ago






          • 1




            No, those definitions are not the same. For example, the PMF of $X$ takes a number as input, but the distribution of $X$ takes a set of numbers as input.
            – littleO
            12 hours ago








          • 1




            Thank you a lot, for clarification. But I am still a bit confused regarding your statement "the distribution of X takes a set of numbers as input". Suppose I flip a coin twice, X is number of tails. My PMF is: 0: 1/4, 1: 1/2, 2, 1/4. So, for each value X (0,1,2,) the PMF returns the P(X=x). How will look the distribution function? It would help you can update your answer. Thanks!
            – John
            12 hours ago






          • 1




            Let $f$ be the distribution of the random variable $X$ that you mentioned. Then for example, if $S = (-7,1.3)$ (that is, $S$ is the open interval from $-7$ to $1.3$), then $f(S) = 3/4$. We could attempt to describe $f$ more explicitly but I'm not sure that it would be useful. The definition of $f$ is just that $f(S) = P(X in S)$ for any set $S subset mathbb R$.
            – littleO
            12 hours ago






          • 1




            OK. I see. Thanks a lot.
            – John
            12 hours ago



















          2














          I'm not aware of an agreed upon definition/meaning for probability distribution.



          On the other hand, probability mass functions and probability density functions have agreed upon definitions and are used to describe probability distributions.



          A probability density function is the generalization of probability mass functions to random variables which are not strictly discrete.
          In the case of a discrete random variable, the main difference is that the probability density function should integrate to one, while the probability mass function should add to one.



          Suppose $X$ is a discrete random variable taking values $S={x_1,x_2,ldots} subset mathbb{R}$.



          The probability mass function is a function $p : Sto [0,1]$ where
          $$
          p(x) = mathbb{P}(X=x)
          $$



          On the other hand, the density function (of any RV) can be thought of as,
          $$
          f(x)dx = mathbb{P}(Xin[x+dx])
          $$

          In integral form you could write this as,
          $$
          int_{x}^{x+dx} f(z)dz = mathbb{P}(Xin [x,x+dx])
          $$



          That is, the density times the width of a small interval gives the probability that $X$ is in that small interval $Xin[x,x+dx]$.



          If the random variable is discrete, then the probability that $X$ is in this interval is the same as the probability $X=x$ for small enough $dx$. So you have $f(x)dx = mathbb{P}(X=x)$ (or in integral form, $lim_{dxto 0}int_{x}^{x+dx} f(z)dz = mathbb{P}(X=x)$).



          In particular, if $p(x)$ is the pmf for a discrete random variable $X$, then we can write the density function as:
          $$
          f(x) = sum_{i:p(x_i)neq 0} p(x_i) delta(x-x_i)
          $$

          where $delta(x)$ is the delta distribution; i.e. $int_a^b f(x)delta(c)d x = f(c)$ whenever $cin[a,b]$






          share|cite|improve this answer



















          • 1




            Only a continuous random variable has a density function. Note that $lim_{dx to 0} int_x^{x+dx} f(z) dz = 0$, so your final equation states that $P(X = x) = 0$. This is true for a continuous random variable but not for a discrete random variable. (Discrete random variables don't have density functions.)
            – littleO
            12 hours ago






          • 1




            You can write the density of discrete random variables using delta distributions.
            – tch
            12 hours ago













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          2 Answers
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          2 Answers
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          active

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          active

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          active

          oldest

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          2














          The word "distribution" gets thrown around loosely sometimes, which can cause confusion.



          The distribution of a random variable $X$ is the function that takes a set $S subset mathbb R$ as input and returns the number $P(X in S)$ as output. (Technically I should assume that $S$ is a "nice" subset of $mathbb R$ in some sense, but let's not worry about that.) I think the Wikipedia article would be more clear if it just gave us this definition up front.



          The PMF of a random variable $X$ is the function that takes a number $x in mathbb R$ as input and returns the number $P(X=x)$ as output. If $X$ is a discrete random variable, then the PMF of $X$ is a convenient way to specify the distribution of $X$.



          Here is one way to describe the relationship between the distribution of $X$ and the PMF of $X$, in the case where $X$ is a discrete random variable. Suppose that the possible values of $X$ are $x_1,x_2,ldots$ If $f$ is the distribution of $X$, then
          $$
          f(S) = sum_{i : x_i in S} P(X = x_i)
          $$

          for any set $S subset mathbb R$.






          share|cite|improve this answer



















          • 1




            Thanks. But is not the definition of PMF by itself exactly as you described for "distribution of a random variable X"? If not, how they are different?
            – John
            13 hours ago






          • 1




            No, those definitions are not the same. For example, the PMF of $X$ takes a number as input, but the distribution of $X$ takes a set of numbers as input.
            – littleO
            12 hours ago








          • 1




            Thank you a lot, for clarification. But I am still a bit confused regarding your statement "the distribution of X takes a set of numbers as input". Suppose I flip a coin twice, X is number of tails. My PMF is: 0: 1/4, 1: 1/2, 2, 1/4. So, for each value X (0,1,2,) the PMF returns the P(X=x). How will look the distribution function? It would help you can update your answer. Thanks!
            – John
            12 hours ago






          • 1




            Let $f$ be the distribution of the random variable $X$ that you mentioned. Then for example, if $S = (-7,1.3)$ (that is, $S$ is the open interval from $-7$ to $1.3$), then $f(S) = 3/4$. We could attempt to describe $f$ more explicitly but I'm not sure that it would be useful. The definition of $f$ is just that $f(S) = P(X in S)$ for any set $S subset mathbb R$.
            – littleO
            12 hours ago






          • 1




            OK. I see. Thanks a lot.
            – John
            12 hours ago
















          2














          The word "distribution" gets thrown around loosely sometimes, which can cause confusion.



          The distribution of a random variable $X$ is the function that takes a set $S subset mathbb R$ as input and returns the number $P(X in S)$ as output. (Technically I should assume that $S$ is a "nice" subset of $mathbb R$ in some sense, but let's not worry about that.) I think the Wikipedia article would be more clear if it just gave us this definition up front.



          The PMF of a random variable $X$ is the function that takes a number $x in mathbb R$ as input and returns the number $P(X=x)$ as output. If $X$ is a discrete random variable, then the PMF of $X$ is a convenient way to specify the distribution of $X$.



          Here is one way to describe the relationship between the distribution of $X$ and the PMF of $X$, in the case where $X$ is a discrete random variable. Suppose that the possible values of $X$ are $x_1,x_2,ldots$ If $f$ is the distribution of $X$, then
          $$
          f(S) = sum_{i : x_i in S} P(X = x_i)
          $$

          for any set $S subset mathbb R$.






          share|cite|improve this answer



















          • 1




            Thanks. But is not the definition of PMF by itself exactly as you described for "distribution of a random variable X"? If not, how they are different?
            – John
            13 hours ago






          • 1




            No, those definitions are not the same. For example, the PMF of $X$ takes a number as input, but the distribution of $X$ takes a set of numbers as input.
            – littleO
            12 hours ago








          • 1




            Thank you a lot, for clarification. But I am still a bit confused regarding your statement "the distribution of X takes a set of numbers as input". Suppose I flip a coin twice, X is number of tails. My PMF is: 0: 1/4, 1: 1/2, 2, 1/4. So, for each value X (0,1,2,) the PMF returns the P(X=x). How will look the distribution function? It would help you can update your answer. Thanks!
            – John
            12 hours ago






          • 1




            Let $f$ be the distribution of the random variable $X$ that you mentioned. Then for example, if $S = (-7,1.3)$ (that is, $S$ is the open interval from $-7$ to $1.3$), then $f(S) = 3/4$. We could attempt to describe $f$ more explicitly but I'm not sure that it would be useful. The definition of $f$ is just that $f(S) = P(X in S)$ for any set $S subset mathbb R$.
            – littleO
            12 hours ago






          • 1




            OK. I see. Thanks a lot.
            – John
            12 hours ago














          2












          2








          2






          The word "distribution" gets thrown around loosely sometimes, which can cause confusion.



          The distribution of a random variable $X$ is the function that takes a set $S subset mathbb R$ as input and returns the number $P(X in S)$ as output. (Technically I should assume that $S$ is a "nice" subset of $mathbb R$ in some sense, but let's not worry about that.) I think the Wikipedia article would be more clear if it just gave us this definition up front.



          The PMF of a random variable $X$ is the function that takes a number $x in mathbb R$ as input and returns the number $P(X=x)$ as output. If $X$ is a discrete random variable, then the PMF of $X$ is a convenient way to specify the distribution of $X$.



          Here is one way to describe the relationship between the distribution of $X$ and the PMF of $X$, in the case where $X$ is a discrete random variable. Suppose that the possible values of $X$ are $x_1,x_2,ldots$ If $f$ is the distribution of $X$, then
          $$
          f(S) = sum_{i : x_i in S} P(X = x_i)
          $$

          for any set $S subset mathbb R$.






          share|cite|improve this answer














          The word "distribution" gets thrown around loosely sometimes, which can cause confusion.



          The distribution of a random variable $X$ is the function that takes a set $S subset mathbb R$ as input and returns the number $P(X in S)$ as output. (Technically I should assume that $S$ is a "nice" subset of $mathbb R$ in some sense, but let's not worry about that.) I think the Wikipedia article would be more clear if it just gave us this definition up front.



          The PMF of a random variable $X$ is the function that takes a number $x in mathbb R$ as input and returns the number $P(X=x)$ as output. If $X$ is a discrete random variable, then the PMF of $X$ is a convenient way to specify the distribution of $X$.



          Here is one way to describe the relationship between the distribution of $X$ and the PMF of $X$, in the case where $X$ is a discrete random variable. Suppose that the possible values of $X$ are $x_1,x_2,ldots$ If $f$ is the distribution of $X$, then
          $$
          f(S) = sum_{i : x_i in S} P(X = x_i)
          $$

          for any set $S subset mathbb R$.







          share|cite|improve this answer














          share|cite|improve this answer



          share|cite|improve this answer








          edited 11 hours ago

























          answered 13 hours ago









          littleO

          29k644108




          29k644108








          • 1




            Thanks. But is not the definition of PMF by itself exactly as you described for "distribution of a random variable X"? If not, how they are different?
            – John
            13 hours ago






          • 1




            No, those definitions are not the same. For example, the PMF of $X$ takes a number as input, but the distribution of $X$ takes a set of numbers as input.
            – littleO
            12 hours ago








          • 1




            Thank you a lot, for clarification. But I am still a bit confused regarding your statement "the distribution of X takes a set of numbers as input". Suppose I flip a coin twice, X is number of tails. My PMF is: 0: 1/4, 1: 1/2, 2, 1/4. So, for each value X (0,1,2,) the PMF returns the P(X=x). How will look the distribution function? It would help you can update your answer. Thanks!
            – John
            12 hours ago






          • 1




            Let $f$ be the distribution of the random variable $X$ that you mentioned. Then for example, if $S = (-7,1.3)$ (that is, $S$ is the open interval from $-7$ to $1.3$), then $f(S) = 3/4$. We could attempt to describe $f$ more explicitly but I'm not sure that it would be useful. The definition of $f$ is just that $f(S) = P(X in S)$ for any set $S subset mathbb R$.
            – littleO
            12 hours ago






          • 1




            OK. I see. Thanks a lot.
            – John
            12 hours ago














          • 1




            Thanks. But is not the definition of PMF by itself exactly as you described for "distribution of a random variable X"? If not, how they are different?
            – John
            13 hours ago






          • 1




            No, those definitions are not the same. For example, the PMF of $X$ takes a number as input, but the distribution of $X$ takes a set of numbers as input.
            – littleO
            12 hours ago








          • 1




            Thank you a lot, for clarification. But I am still a bit confused regarding your statement "the distribution of X takes a set of numbers as input". Suppose I flip a coin twice, X is number of tails. My PMF is: 0: 1/4, 1: 1/2, 2, 1/4. So, for each value X (0,1,2,) the PMF returns the P(X=x). How will look the distribution function? It would help you can update your answer. Thanks!
            – John
            12 hours ago






          • 1




            Let $f$ be the distribution of the random variable $X$ that you mentioned. Then for example, if $S = (-7,1.3)$ (that is, $S$ is the open interval from $-7$ to $1.3$), then $f(S) = 3/4$. We could attempt to describe $f$ more explicitly but I'm not sure that it would be useful. The definition of $f$ is just that $f(S) = P(X in S)$ for any set $S subset mathbb R$.
            – littleO
            12 hours ago






          • 1




            OK. I see. Thanks a lot.
            – John
            12 hours ago








          1




          1




          Thanks. But is not the definition of PMF by itself exactly as you described for "distribution of a random variable X"? If not, how they are different?
          – John
          13 hours ago




          Thanks. But is not the definition of PMF by itself exactly as you described for "distribution of a random variable X"? If not, how they are different?
          – John
          13 hours ago




          1




          1




          No, those definitions are not the same. For example, the PMF of $X$ takes a number as input, but the distribution of $X$ takes a set of numbers as input.
          – littleO
          12 hours ago






          No, those definitions are not the same. For example, the PMF of $X$ takes a number as input, but the distribution of $X$ takes a set of numbers as input.
          – littleO
          12 hours ago






          1




          1




          Thank you a lot, for clarification. But I am still a bit confused regarding your statement "the distribution of X takes a set of numbers as input". Suppose I flip a coin twice, X is number of tails. My PMF is: 0: 1/4, 1: 1/2, 2, 1/4. So, for each value X (0,1,2,) the PMF returns the P(X=x). How will look the distribution function? It would help you can update your answer. Thanks!
          – John
          12 hours ago




          Thank you a lot, for clarification. But I am still a bit confused regarding your statement "the distribution of X takes a set of numbers as input". Suppose I flip a coin twice, X is number of tails. My PMF is: 0: 1/4, 1: 1/2, 2, 1/4. So, for each value X (0,1,2,) the PMF returns the P(X=x). How will look the distribution function? It would help you can update your answer. Thanks!
          – John
          12 hours ago




          1




          1




          Let $f$ be the distribution of the random variable $X$ that you mentioned. Then for example, if $S = (-7,1.3)$ (that is, $S$ is the open interval from $-7$ to $1.3$), then $f(S) = 3/4$. We could attempt to describe $f$ more explicitly but I'm not sure that it would be useful. The definition of $f$ is just that $f(S) = P(X in S)$ for any set $S subset mathbb R$.
          – littleO
          12 hours ago




          Let $f$ be the distribution of the random variable $X$ that you mentioned. Then for example, if $S = (-7,1.3)$ (that is, $S$ is the open interval from $-7$ to $1.3$), then $f(S) = 3/4$. We could attempt to describe $f$ more explicitly but I'm not sure that it would be useful. The definition of $f$ is just that $f(S) = P(X in S)$ for any set $S subset mathbb R$.
          – littleO
          12 hours ago




          1




          1




          OK. I see. Thanks a lot.
          – John
          12 hours ago




          OK. I see. Thanks a lot.
          – John
          12 hours ago











          2














          I'm not aware of an agreed upon definition/meaning for probability distribution.



          On the other hand, probability mass functions and probability density functions have agreed upon definitions and are used to describe probability distributions.



          A probability density function is the generalization of probability mass functions to random variables which are not strictly discrete.
          In the case of a discrete random variable, the main difference is that the probability density function should integrate to one, while the probability mass function should add to one.



          Suppose $X$ is a discrete random variable taking values $S={x_1,x_2,ldots} subset mathbb{R}$.



          The probability mass function is a function $p : Sto [0,1]$ where
          $$
          p(x) = mathbb{P}(X=x)
          $$



          On the other hand, the density function (of any RV) can be thought of as,
          $$
          f(x)dx = mathbb{P}(Xin[x+dx])
          $$

          In integral form you could write this as,
          $$
          int_{x}^{x+dx} f(z)dz = mathbb{P}(Xin [x,x+dx])
          $$



          That is, the density times the width of a small interval gives the probability that $X$ is in that small interval $Xin[x,x+dx]$.



          If the random variable is discrete, then the probability that $X$ is in this interval is the same as the probability $X=x$ for small enough $dx$. So you have $f(x)dx = mathbb{P}(X=x)$ (or in integral form, $lim_{dxto 0}int_{x}^{x+dx} f(z)dz = mathbb{P}(X=x)$).



          In particular, if $p(x)$ is the pmf for a discrete random variable $X$, then we can write the density function as:
          $$
          f(x) = sum_{i:p(x_i)neq 0} p(x_i) delta(x-x_i)
          $$

          where $delta(x)$ is the delta distribution; i.e. $int_a^b f(x)delta(c)d x = f(c)$ whenever $cin[a,b]$






          share|cite|improve this answer



















          • 1




            Only a continuous random variable has a density function. Note that $lim_{dx to 0} int_x^{x+dx} f(z) dz = 0$, so your final equation states that $P(X = x) = 0$. This is true for a continuous random variable but not for a discrete random variable. (Discrete random variables don't have density functions.)
            – littleO
            12 hours ago






          • 1




            You can write the density of discrete random variables using delta distributions.
            – tch
            12 hours ago


















          2














          I'm not aware of an agreed upon definition/meaning for probability distribution.



          On the other hand, probability mass functions and probability density functions have agreed upon definitions and are used to describe probability distributions.



          A probability density function is the generalization of probability mass functions to random variables which are not strictly discrete.
          In the case of a discrete random variable, the main difference is that the probability density function should integrate to one, while the probability mass function should add to one.



          Suppose $X$ is a discrete random variable taking values $S={x_1,x_2,ldots} subset mathbb{R}$.



          The probability mass function is a function $p : Sto [0,1]$ where
          $$
          p(x) = mathbb{P}(X=x)
          $$



          On the other hand, the density function (of any RV) can be thought of as,
          $$
          f(x)dx = mathbb{P}(Xin[x+dx])
          $$

          In integral form you could write this as,
          $$
          int_{x}^{x+dx} f(z)dz = mathbb{P}(Xin [x,x+dx])
          $$



          That is, the density times the width of a small interval gives the probability that $X$ is in that small interval $Xin[x,x+dx]$.



          If the random variable is discrete, then the probability that $X$ is in this interval is the same as the probability $X=x$ for small enough $dx$. So you have $f(x)dx = mathbb{P}(X=x)$ (or in integral form, $lim_{dxto 0}int_{x}^{x+dx} f(z)dz = mathbb{P}(X=x)$).



          In particular, if $p(x)$ is the pmf for a discrete random variable $X$, then we can write the density function as:
          $$
          f(x) = sum_{i:p(x_i)neq 0} p(x_i) delta(x-x_i)
          $$

          where $delta(x)$ is the delta distribution; i.e. $int_a^b f(x)delta(c)d x = f(c)$ whenever $cin[a,b]$






          share|cite|improve this answer



















          • 1




            Only a continuous random variable has a density function. Note that $lim_{dx to 0} int_x^{x+dx} f(z) dz = 0$, so your final equation states that $P(X = x) = 0$. This is true for a continuous random variable but not for a discrete random variable. (Discrete random variables don't have density functions.)
            – littleO
            12 hours ago






          • 1




            You can write the density of discrete random variables using delta distributions.
            – tch
            12 hours ago
















          2












          2








          2






          I'm not aware of an agreed upon definition/meaning for probability distribution.



          On the other hand, probability mass functions and probability density functions have agreed upon definitions and are used to describe probability distributions.



          A probability density function is the generalization of probability mass functions to random variables which are not strictly discrete.
          In the case of a discrete random variable, the main difference is that the probability density function should integrate to one, while the probability mass function should add to one.



          Suppose $X$ is a discrete random variable taking values $S={x_1,x_2,ldots} subset mathbb{R}$.



          The probability mass function is a function $p : Sto [0,1]$ where
          $$
          p(x) = mathbb{P}(X=x)
          $$



          On the other hand, the density function (of any RV) can be thought of as,
          $$
          f(x)dx = mathbb{P}(Xin[x+dx])
          $$

          In integral form you could write this as,
          $$
          int_{x}^{x+dx} f(z)dz = mathbb{P}(Xin [x,x+dx])
          $$



          That is, the density times the width of a small interval gives the probability that $X$ is in that small interval $Xin[x,x+dx]$.



          If the random variable is discrete, then the probability that $X$ is in this interval is the same as the probability $X=x$ for small enough $dx$. So you have $f(x)dx = mathbb{P}(X=x)$ (or in integral form, $lim_{dxto 0}int_{x}^{x+dx} f(z)dz = mathbb{P}(X=x)$).



          In particular, if $p(x)$ is the pmf for a discrete random variable $X$, then we can write the density function as:
          $$
          f(x) = sum_{i:p(x_i)neq 0} p(x_i) delta(x-x_i)
          $$

          where $delta(x)$ is the delta distribution; i.e. $int_a^b f(x)delta(c)d x = f(c)$ whenever $cin[a,b]$






          share|cite|improve this answer














          I'm not aware of an agreed upon definition/meaning for probability distribution.



          On the other hand, probability mass functions and probability density functions have agreed upon definitions and are used to describe probability distributions.



          A probability density function is the generalization of probability mass functions to random variables which are not strictly discrete.
          In the case of a discrete random variable, the main difference is that the probability density function should integrate to one, while the probability mass function should add to one.



          Suppose $X$ is a discrete random variable taking values $S={x_1,x_2,ldots} subset mathbb{R}$.



          The probability mass function is a function $p : Sto [0,1]$ where
          $$
          p(x) = mathbb{P}(X=x)
          $$



          On the other hand, the density function (of any RV) can be thought of as,
          $$
          f(x)dx = mathbb{P}(Xin[x+dx])
          $$

          In integral form you could write this as,
          $$
          int_{x}^{x+dx} f(z)dz = mathbb{P}(Xin [x,x+dx])
          $$



          That is, the density times the width of a small interval gives the probability that $X$ is in that small interval $Xin[x,x+dx]$.



          If the random variable is discrete, then the probability that $X$ is in this interval is the same as the probability $X=x$ for small enough $dx$. So you have $f(x)dx = mathbb{P}(X=x)$ (or in integral form, $lim_{dxto 0}int_{x}^{x+dx} f(z)dz = mathbb{P}(X=x)$).



          In particular, if $p(x)$ is the pmf for a discrete random variable $X$, then we can write the density function as:
          $$
          f(x) = sum_{i:p(x_i)neq 0} p(x_i) delta(x-x_i)
          $$

          where $delta(x)$ is the delta distribution; i.e. $int_a^b f(x)delta(c)d x = f(c)$ whenever $cin[a,b]$







          share|cite|improve this answer














          share|cite|improve this answer



          share|cite|improve this answer








          edited 11 hours ago

























          answered 13 hours ago









          tch

          34919




          34919








          • 1




            Only a continuous random variable has a density function. Note that $lim_{dx to 0} int_x^{x+dx} f(z) dz = 0$, so your final equation states that $P(X = x) = 0$. This is true for a continuous random variable but not for a discrete random variable. (Discrete random variables don't have density functions.)
            – littleO
            12 hours ago






          • 1




            You can write the density of discrete random variables using delta distributions.
            – tch
            12 hours ago
















          • 1




            Only a continuous random variable has a density function. Note that $lim_{dx to 0} int_x^{x+dx} f(z) dz = 0$, so your final equation states that $P(X = x) = 0$. This is true for a continuous random variable but not for a discrete random variable. (Discrete random variables don't have density functions.)
            – littleO
            12 hours ago






          • 1




            You can write the density of discrete random variables using delta distributions.
            – tch
            12 hours ago










          1




          1




          Only a continuous random variable has a density function. Note that $lim_{dx to 0} int_x^{x+dx} f(z) dz = 0$, so your final equation states that $P(X = x) = 0$. This is true for a continuous random variable but not for a discrete random variable. (Discrete random variables don't have density functions.)
          – littleO
          12 hours ago




          Only a continuous random variable has a density function. Note that $lim_{dx to 0} int_x^{x+dx} f(z) dz = 0$, so your final equation states that $P(X = x) = 0$. This is true for a continuous random variable but not for a discrete random variable. (Discrete random variables don't have density functions.)
          – littleO
          12 hours ago




          1




          1




          You can write the density of discrete random variables using delta distributions.
          – tch
          12 hours ago






          You can write the density of discrete random variables using delta distributions.
          – tch
          12 hours ago




















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