A function cannot be a probability mass function (PMF) if it violates the properties of a PMF. A PMF must assign a non-negative probability to each possible outcome of a discrete random variable, and the sum of probabilities for all possible outcomes must be equal to 1. If a function does not satisfy these properties, it cannot be considered a PMF.
It is a function which is usually used with continuous distributions, to give the probability associated with different values of the variable.
I expect you mean the probability mass function (pmf). Please see the right sidebar in the linked page.
Yes- the highest probability value is the mode. Let me clarify this answer: For a probability mass function for a discrete variables, the mode is the value with the highest probability as shown on the y axis. For a probability density function for continuous variables, the mode is the value with the highest probability density as shown on the y-axis.
None. The full name is the Probability Distribution Function (pdf).
They are the same. The full name is the Probability Distribution Function (pdf).
It is a function which is usually used with continuous distributions, to give the probability associated with different values of the variable.
The probability mass function is used to characterize the distribution of discrete random variables, while the probability density function is used to characterize the distribution of absolutely continuous random variables. You might want to read more about this at www.statlect.com/prbdst1.htm (see the link below or on the right)
I expect you mean the probability mass function (pmf). Please see the right sidebar in the linked page.
Yes- the highest probability value is the mode. Let me clarify this answer: For a probability mass function for a discrete variables, the mode is the value with the highest probability as shown on the y axis. For a probability density function for continuous variables, the mode is the value with the highest probability density as shown on the y-axis.
None. The full name is the Probability Distribution Function (pdf).
They are the same. The full name is the Probability Distribution Function (pdf).
A probability density function assigns a probability value for each point in the domain of the random variable. The probability distribution assigns the same probability to subsets of that domain.
The probability distribution function.
Yes.
No. f is a letter of the Roman alphabet. It cannot be a probability density function.
The marginal probability distribution function.
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